EMR Strategy [H1 Backtesting]EMR Strategy base on EMA, MACD and RSI to supply signal on time frame H1.
Details of Rule as below:
===
1.EMA
+ Time frame: H1
+ Periods: 25, 100 (~ EMA 25 H4), 600 (~ EMA 25 D1)
===
2.MACD
+ Time frame: H1
+ Periods: 12,26,9
===
3.RSI
+ Time frame: H1
+ Periods: 14
===
4.Trading Rule
4.1.Long Position
+ MACD>0 and RSI>50 and close price moving above EMA 25
+ Close price crossed EMA 100 or crossed EMA 600 at the first time
4.2.Short Position
+ MACD<0 and RSI<50 and close price moving below EMA 25
+ Close price crossed EMA 100 or crossed EMA 600 at the first time
===
5.Money Management
+ This strategy concentrate into winrate.
+ So use trailing stop to protect your profits.
+ And use stoploss to avoid big loss on trades.
Recherche dans les scripts pour "汇丰股票25"
CHOP Zone Entry Strategy + DMI/PSAR ExitThis is a Strategy with associated visual indicators and Long/Short and Reverse/Close Position Alerts for the Choppiness Index (CHOP) . It is used to determine if the market is choppy (trading sideways) or not choppy (trading within a trend in either direction). CHOP is not directional, so a DMI script was ported into this strategy to allow for trend confirmation and direction determination; it consists of an Average Directional Index (ADX) , Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) . In addition, a Parabolic SAR is also included to act as a trailing stop during any strong trends.
Development Notes
---------------------------
This indicator, and most of the descriptions below, were derived largely from the TradingView reference manual. Feedback and suggestions for improvement are more than welcome, as well are recommended Input settings and best practices for use.
www.tradingview.com
www.tradingview.com
www.tradingview.com
Recommend using the below DMI and PSAR indicators in conjunction with this script to fully visualize and understand how entry and exit conditions are chosen. Variable inputs should correlate between the scripts for uniformity and visual compatibility.
THANKS to LazyBear and his Momentum Squeeze script for helping me quickly develop a momentum state model for coloring the Chop line by trend.
Strategy Description
---------------------------
CHOP produces values that determine whether the market is choppy or trending . The closer the value is to 100 , the higher the choppiness levels , while the closer it is to 0 , the stronger the market is trending . Territories for both levels, and their associated upper and lower thresholds, are popularly defined using the Fibonacci Retracements, 61.8 and 38.2.
Basic Use
---------------------------
CHOP is often used to confirm the market condition to help you stay out of sideways markets and only enter when there is movement or imminent explosions. When readings are above the upper threshold, continued sideways movement may be expected, while readings below the lower threshold are typically indicative of a continuing trend. It is also used to anticipate upcoming trendiness changes, with the general belief that extended periods of consolidation (sideways movement) are followed by extended periods of strong, trending, directional movement, and vice versa.
One limitation in this index is that you must be cautious in deciding whether the range or trend will likely continue, or if it will reverse.
Confidence in price action and trend is higher when two or more indicators are in agreement -- while this strategy combines CHOP with both DMI and PSAR, we would still recommend pairing with other indicators to determine entry or exit trade opportunities.
Recommend also choosing 'Once Per Bar Close' when creating alerts.
Inputs
---------------------------
Strategy Direction - an option to only trade Short, Long, Both, or only in the direction of the Trend (Follow Trend is the Default).
Sensitivity - an incremental variable to test whether the past n candles are in the same trend state before triggering a delayed long or short alert (1 is the Default). Can help filter out noise and reduces active alerts.
Show Chop Index - two visual styles are provided for user preference, a visible Chop line with a background overlay, or a compact column and label only view.
Chop Lookback Period - the time period to be used in calculating CHOP (14 is the Default).
Chop Offset - changing this number will move the CHOP either forwards or backwards relative to the current market (0 is the Default).
Smooth Chop Line and Length - if enabled, the entered time period will be used in calculating a smooth average of the index (Enabled and 4 are the Defaults).
Color Line to Trend Direction - toggles whether the index line is colored to visually depict the current trend direction (Enabled is the Default).
Color Background - toggles the visibility of a background color based on the index state (Enabled is the Default).
Enable DMI Option - if enabled, then entry will be confirmed by and dependent on the ADX Key Level, with any close or reversal confirmed by both ADX and +/-DI to determine whether there is a strong trend present or not (Enabled is the Default).
ADX Smoothing - the time period to be used in calculating the ADX which has a smoothing component (14 is the Default).
DI Length - the time period to be used in calculating the DI (14 is the Default).
ADX Key Level - any trade with the ADX above the key level is a strong indicator that it is trending (23 to 25 is the suggested setting).
Enable PSAR Option - enables trailing stop loss orders (Enabled is the Default).
PSAR Start - the starting value for the Acceleration Force (0.015 is our chosen Default, 0.02 is more common).
PSAR Increment - the increment in which the Acceleration Force will move (0.001 is our chosen Default, 0.02 is more common).
PSAR Max Value - the maximum value of the Acceleration Factor (0.2 is the Default).
Color Candles Option - an option to transpose the CHOP condition levels to the main candle bars. Note that the outer red and green border will still be distinguished by whether each individual candle is bearish or bullish during the specified timeframe.
Note too that if both DMI and PSAR are deselected, then close determinations will default to a CHOP reversal strategy (e.g., close long when below 38.2 and close short when above 61.8). Though if either DMI or PSAR are enabled, then the CHOP reversal for close determination will automatically be disabled.
Indicator Visuals
---------------------------
For the candle colors, black indicates tight chop (45 to 55), yellow is loose chop (38.2 to 45 and 55 to 61.8), dark purple is trending down (< 38.2), and dark blue is trending up (> 61.8).
The background color has additional shades to differentiate a wider range of more levels…
• < 30 is dark purple
• 30 to 38.2 is purple
• 38.2 to 45 is light purple
• 45 to 55 is black
• 55 to 61.8 is light blue
• 61.8 to 70 is blue
• > 70 is dark blue
Long, Short, Close, and Reverse labels are plotted on the Chop line, which itself can be colored based on the trend. The chop line can also be hidden for a clean and compact, columnar view, which is my preferred option (see example image below).
Visual cues are intended to improve analysis and decrease interpretation time during trading, as well as to aid in understanding the purpose of this strategy and how its inclusion can benefit a comprehensive trading plan.
DMI and Trend Strength
---------------------------
To analyze trend strength, the focus should be on the ADX line and not the +DI or -DI lines. An ADX reading above 25 indicates a strong trend , while a reading below 20 indicates a weak or non-existent trend . A reading between those two values would be considered indeterminable. Though what is truly a strong trend or a weak trend depends on the financial instrument being examined; historical analysis can assist in determining appropriate values.
DMI exits trade when ADX is below the user selected key level (e.g., default is 25) and when the +/- DI lines cross (e.g., -DI > +DI exits long position and +DI > -DI exits short position).
PSAR and Trailing Stop
---------------------------
PSAR is a time and price based indicator that excels at measuring direction and duration, though not the actual strength of a trend, which is why we use this in conjunction with DMI. It is also included in this script as a trailing stop option to maximize gains during strong trends and to mitigate any false ADX strengthening signals.
This creates a parabola that is located below the candle during a Bullish trend and above during a Bearish trend. A buy or reversal is signaled when the price crosses above or below the Parabolic SAR.
Long/Short Entry
---------------------------
1. CHOP must be over 61.8 (long) or under 38.2 (short).
2. If DMI is enabled, then the ADX signal line must be above the user selected Key Level (default is 25).
3. If Sensitivity is selected, then that past candle must meet the criteria in step 1, as well as all the intermediate candles in between.
4. If "Follow Trend" is selected and PSAR is enabled, then a long position can only open when the momentum and PSAR are in an uptrend, or short when both are in a downtrend, to include all intermediate candles if the Sensitivity option is set on a past candle.
Close/Reverse
---------------------------
1. If DMI is enabled, then a close flag will be raised when the ADX signal drops below the Key Level (of 25), and -DI crosses over +DI (if long), or +DI crosses over -DI (if short).
2. If PSAR is enabled, then a close flag will be raised when the current trend state is opposite the last state.
3. If both DMI and PSAR are disabled, then a close flag will be raised if the Chop line drops under 38.2 (if long) or goes over 61.8 (if short).
4. If a Long or Short Entry is triggered on the same candle as any of the above close flags, then the position will be reversed, else the position will be closed.
Strategy Alerts
---------------------------
1. Long Entry
2. Short Entry
3. Reverse
4. Close
The provided backtest result is based on a position sizing of 10% equity with 100k initial capital. When testing SPX, disabling the DMI performed the best, but EURUSD performed poorly without it enabled, and TSLA had a small reduction in net profit. Timeframe likewise differed between commodities with TSLA performing best at 30M, SPX at 15M, and EURUSD at 4H. I do not plan on using this as a standalone strategy, but I also was expecting better results with the inclusion of EMI and PSAR to compliment the CHOP. Key elements of this script will likely be included in future, more holistic strategies.
Disclaimer
---------------------------
Past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script are not intended to provide any financial advice. Trade at your own risk.
No known repainting, though there may be if an offset is introduced in the Inputs. I did my best not to code any other variables that repaint, but cannot fully attest to this fact.
Dual SuperTrend, Ichimoku and DMI Color Weighted by DGTThis study interprets SuperTrend with Ichimoku Cloud, one of the popular technical analysis indicator, and interprets Directional Movement (DMI), which is another quite valuable technical analysis indicator.
Then combines the interpreted SuperTrend with interpreted Directional Movement (DMI) and Volume Based Colored Bars indicator created by Kıvaç ÖZBİLGİÇ (permission has been granted from the author)
Here are details of the concept applied
1- SuperTrend Line colored based on Ichimoku Cloud
Definition
The Ichimoku Cloud, developed by Goichi Hosoda and published in the late 1960s, is a collection of technical indicators that give it a unique capacity to show support and resistance levels, momentum and trend direction
What Does the Ichimoku Cloud Tells?
The overall trend is up when price is above the cloud, known as Kumo Cloud, down when price is below the Kumo Cloud, and trendless or transitioning when price is in the Kumo Cloud
When Senkou Span A (Leading Span A) is rising and above Senkou Span B (Leading Span B), this helps confirm the uptrend and space between the lines is typically colored green. When Senkou Span A is falling and below Senkou Span B, this helps confirm the downtrend. The space between the lines is typically colored red
Traders often use the Kumo Cloud as an area of support and resistance depending on the relative location of the price. The Kumo Cloud provides support/resistance levels that can be projected into the future. This sets the Ichimoku Cloud apart from many other technical indicators that only provide support and resistance levels for the current date and time
Crossovers, also known as TK Cross among Ichimoku Cloud traders, are another way the indicator can be used. Watch for the Tenkan-Sen Line, or Conversion Line, to move above the Kijun-Sen Line, or Base Line, especially when price is above the Kumo cloud. This can be a powerful buy signal. One option is to hold the trade until the Tenkan-Sen drops back below the Kijun-Sen Line. Any of the other lines could be used as exit points as well.
With this study:
Allow Traders to use the Ichimoku Cloud in conjunction with other technical indicators to maximize their risk-adjusted returns
The Ichimoku Cloud can make a chart look busy with all the lines. To Remedy this a different approach is applied in this study showing the Price and the Kumo Cloud relation as well as TK Crosses displayed. The SuperTrend Indicator is chosen to display Ichimoku Indicator, where the SuperTrend is another trend following indicator.
How it works:
SuperTrend Line is colored as:
Green when the Price is above the Kumo Cloud
Red when the Price is below the Kumo Cloud
Black when the Price is within the Kumo Cloud
And Finally Blue when the Kumo Cloud Is not ready to be drawn or not Kumo Cloud available
Additionally intensity of the colors used in all cases above are defined by values of Tenkan-Sen and Kijun-Sen Line, which allows us to detect TK Crosses
2- Plots Colored Directional Movement Line
Definition
Directional Movement (DMI) (created by J. Welles Wilder ) is actually a collection of three separate indicators combined into one. Directional Movement consists of the Average Directional Index (ADX) , Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) . ADX's purposes is to define whether or not there is a trend present. It does not take direction into account at all. The other two indicators (+DI and -DI) are used to compliment the ADX. They serve the purpose of determining trend direction. By combining all three, a technical analyst has a way of determining and measuring a trend's strength as well as its direction.
This study combines all three lines in a single colored shapes series plotted on the top of the price chart indicating the trend strength with different colors and its direction with triangle up and down shapes.
What to look for
Trend Strength : Analyzing trend strength is the most basic use for the DMI. Wilder believed that a DMI reading above 25 indicated a strong trend, while a reading below 20 indicated a weak or non-existent trend
Crosses : DI Crossovers are the significant trading signal generated by the DMI
With this study
A Strong Trend is assumed when ADX >= 25
Bullish Trend is defined as (+D I > -DI ) and (ADX >= 25), which is plotted as green triangle up shape on top of the price chart
Bearish Trend is defined as (+D I < -DI ) and (ADX >= 25), which is plotted as red triangle down shape on top of the price chart
Week Trend is assumed when 17< ADX < 25, which is plotted as black triangles up or down shape, depending on +DI-DI values, on top of the price chart
Non-Existent Trend is assumed when ADX < 17, which is plotted as yellow triangles up or down shape, depending on +DI-DI values, on top of the price chart
Additionally intensity of the colors used in all cases above are defined by comparing ADX’s current value with its previous value
3- Volume Based Colored Bars indicator created by Kıvaç ÖZBİLGİÇ
Volume Based Colored Bars colors the bars into volume weighted signals increasing the visibility of the Volume changes. Intensity of the colors of the bars varies according to average value of the volume for given length of bars (default value set to 30 bars)
Disclaimer: The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd tradingview user liable for any possible claim for damages arising from any decision you make based on use of the script
Colored Directional Movement and Bollinger Band's Cloud by DGTThis study combines Bollinger Bands, one of the most popular technical analysis indicators on the market, and Directional Movement (DMI), which is another quite valuable technical analysis indicator.
Bollinger Bands used in conjunction with Directional Movement (DMI) may help getting a better understanding of the ever changing landscape of the market and perform more advanced technical analysis
Here are details of the concept applied
1- Plots Bollinger Band’s (BB) Cloud colored based on Bollinger Band Width (BBW) Indicator’s value
Definition
Bollinger Bands (created by John Bollinger ) are a way to measure volatility . As volatility increases, the wider the bands become and similarly as volatility decreases, the gap between bands narrows
Bollinger Bands, in widely used approach, consist of a band of three lines. Likewise common usage In this study a band of five lines is implemented
The line in the middle is a Simple Moving Average (SMA) set to a period of 20 bars (the most popular usage). The SMA then serves as a base for the Upper and Lower Bands. The Upper and Lower Bands are used as a way to measure volatility by observing the relationship between the Bands and price. the Upper and Lower Bands in this study are set to two and three standard deviations (widely used form is only two standard deviations) away from the SMA (The Middle Line), hence there are two Upper Bands and two Lower Bands. The background between two Upper Bands is filled with a green color and the background between two Lower Bands is filled with a red color. In this we have obtained Bollinger Band’s (BB) Clouds (Upper Cloud and Lower Cloud)
Additionally the intensity of the color of the background is calculated with Bollinger Bands Width ( BBW ), which is a technical analysis indicator derived from the standard Bollinger Bands indicator. Bollinger Bands Width, quantitatively measures the width between the Upper and Lower Bands. In this study the intensity of the color of the background is increased if BBW value is greater than %25
What to look for
Price Actions : Prices are almost always within the bands especially at this study the bands of three standard deviations away from the SMA. Price touching or breaking the BB Clouds could be considered as buying or selling opportunity. However this is not always the case, there are exceptions such as Walking the Bands. “Walking the Bands” can occur in either a strong uptrend or a strong downtrend. During a strong trend, there may be repeated instances of price touching or breaking through the BB Clouds. Each time that this occurs, it is not a signal, it is a result of the overall strength of the move. In this study in order to get a better understanding of the trend and add ability to perform some advanced technical analysis Directional Movement Indicator (DMI) is added to be used in conjunction with Bollinger Bands.
Cycling Between Expansion and Contraction : One of the most well-known theories in regards to Bollinger Bands is that volatility typically fluctuates between periods of expansion (Bands Widening : surge in volatility and price breaks through the BB Cloud) and contraction (Bands Narrowing : low volatility and price is moving relatively sideways). Using Bollinger Bands in conjunction with Bollinger Bands Width may help identifying beginning of a new directional trend which can result in some nice buying or selling signals. Of course the trader should always use caution
2- Plots Colored Directional Movement Line
Definition
Directional Movement (DMI) (created by J. Welles Wilder ) is actually a collection of three separate indicators combined into one. Directional Movement consists of the Average Directional Index (ADX) , Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) . ADX's purposes is to define whether or not there is a trend present. It does not take direction into account at all. The other two indicators (+DI and -DI) are used to compliment the ADX. They serve the purpose of determining trend direction. By combining all three, a technical analyst has a way of determining and measuring a trend's strength as well as its direction.
This study combines all three lines in a single colored shapes series plotted on the top of the price chart indicating the trend strength with different colors and its direction with triangle up and down shapes.
What to look for
Trend Strength : Analyzing trend strength is the most basic use for the DMI. Wilder believed that a DMI reading above 25 indicated a strong trend, while a reading below 20 indicated a weak or non-existent trend
Crosses : DI Crossovers are the significant trading signal generated by the DMI
With this study
A Strong Trend is assumed when ADX >= 25
Bullish Trend is defined as (+D I > -DI ) and (ADX >= 25), which is plotted as green triangle up shape on top of the price chart
Bearish Trend is defined as (+D I < -DI ) and (ADX >= 25), which is plotted as red triangle down shape on top of the price chart
Week Trend is assumed when 17< ADX < 25, which is plotted as black triangles up or down shape, depending on +DI-DI values, on top of the price chart
Non-Existent Trend is assumed when ADX < 17, which is plotted as yellow triangles up or down shape, depending on +DI-DI values, on top of the price chart
Additionally intensity of the colors used in all cases above are defined by comparing ADX’s current value with its previous value
Summary of the Study:
Even more simplified and visually enhanced DMI drawing comparing to its classical usage (may require a bit practice to get used to it)
As said previously, to get a better understanding of the trend and add ability to perform some advanced technical analysis Directional Movement Indicator (DMI) is used in conjunction with Bollinger Bands.
PS: Analysis and tests are performed with high volatile Cryptocurrency Market
Source of References : definitions provided herein are gathered from TradingView’s knowledgebase/library
Disclaimer: The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd tradingview user liable for any possible claim for damages arising from any decision you make based on use of the script
EMAs CloudEmas clouds is based on exponential moving averages 25 , 50, 100, 200
Ema 25/50 when crossed give you entry to the trend based on dipacmend forward ema 25/50 for 25 candles and ema 100/200
I add it also momentum based on close of the price moved back 25 candles.
YM Ultimate SNIPER v5# YM Ultimate SNIPER v5 - Documentation & Trading Guide
## 🎯 Unified GRA + DeepFlow | YM/MYM Optimized
**TARGET: 3-7 High-Confluence Trades per Day**
---
## ⚡ QUICK START
```
┌─────────────────────────────────────────────────────────────────┐
│ YM ULTIMATE SNIPER v5 │
├─────────────────────────────────────────────────────────────────┤
│ │
│ SIGNALS: │
│ S🎯 = S-Tier (50+ pts) → HOLD position │
│ A🎯 = A-Tier (25-49 pts) → SWING trade │
│ B🎯 = B-Tier (12-24 pts) → SCALP quick │
│ Z = Zone entry (price at FVG zone) │
│ │
│ SESSIONS (ET): │
│ LDN = 3:00-5:00 AM (London) │
│ NY = 9:30-11:30 AM (New York Open) │
│ PWR = 3:00-4:00 PM (Power Hour) │
│ │
│ COLORS: │
│ 🟩 Green zones = Bullish FVG (buy zone) │
│ 🟥 Red zones = Bearish FVG (sell zone) │
│ 🟣 Purple lines = Single prints (S/R levels) │
│ │
│ TABLE (Top Right): │
│ Pts = Candle point range │
│ Tier = S/A/B/X classification │
│ Vol = Volume ratio (green = good) │
│ Delta = Buy/Sell dominance │
│ Sess = Current session │
│ Zone = In FVG zone status │
│ Score = Confluence score /10 │
│ CVD = Cumulative delta direction │
│ R:R = Risk:Reward ratio │
│ │
└─────────────────────────────────────────────────────────────────┘
```
---
## 📋 VERSION 5 CHANGES
### What's New
- **Removed all imbalance code** - caused compilation errors
- **Simplified delta analysis** - uses candle structure instead of intrabar data
- **Cleaner confluence scoring** - 5 clear factors, max 10 points
- **Reliable table** - updates on last bar only, no flickering
- **Works on YM and MYM** - same logic applies to micro contracts
### Removed Features
- Candle-anchored imbalance markers
- Imbalance S/R zones
- Intrabar volume profile analysis
- POC visualization
### Kept & Improved
- Tier classification (S/A/B)
- FVG zone detection & visualization
- Single print detection
- Session windows with backgrounds
- Confluence scoring
- Stop/Target auto-calculation
- All alerts
---
## 🎯 SIGNAL TYPES
### Tier Signals (S🎯, A🎯, B🎯)
These are high-confluence signals that pass all filters:
| Tier | Points | Value/Contract | Action | Hold Time |
|------|--------|----------------|--------|-----------|
| **S** | 50+ | $250+ | HOLD | 2-5 min |
| **A** | 25-49 | $125-245 | SWING | 1-3 min |
| **B** | 12-24 | $60-120 | SCALP | 30-90 sec |
**Filters Required:**
1. Tier threshold met (points)
2. Volume ≥ 1.8x average
3. Delta dominance ≥ 62%
4. Body ratio ≥ 70%
5. Range ≥ 1.3x average
6. Proper wicks (no reversal wicks)
7. CVD confirmation (optional)
8. In trading session
### Zone Signals (Z)
Zone entries trigger when:
- Price is inside an FVG zone
- Delta shows dominance in zone direction
- Volume is above average
- In active session
- No tier signal already present
---
## 📊 CONFLUENCE SCORING
**Maximum Score: 10 points**
| Factor | Points | Condition |
|--------|--------|-----------|
| Tier | 1-3 | B=1, A=2, S=3 |
| In Zone | +2 | Price inside FVG zone |
| Strong Volume | +2 | Volume ≥ 2x average |
| Strong Delta | +2 | Delta ≥ 70% |
| CVD Momentum | +1 | CVD trending with signal |
**Score Interpretation:**
- **7-10**: Elite setup - full size
- **5-6**: Good setup - standard size
- **4**: Minimum threshold - reduced size
- **< 4**: No signal shown
---
## ⏰ SESSION WINDOWS
### London (3:00-5:00 AM ET)
- European institutional flow
- Character: Slow build-up, clean trends
- Expected trades: 1-2
- Best for: Zone entries, A/B tier
### NY Open (9:30-11:30 AM ET)
- Highest volume, most institutional activity
- Character: Initial balance, breakouts
- Expected trades: 2-3
- Best for: S/A tier, zone confluence
### Power Hour (3:00-4:00 PM ET)
- End-of-day rebalancing, MOC orders
- Character: Mean reversion or trend acceleration
- Expected trades: 1-2
- Best for: Zone entries, B tier scalps
---
## 🟩 FVG ZONES
### What Are FVG Zones?
Fair Value Gaps (FVGs) are price gaps between candles where price moved so fast that a gap was left. These gaps often act as support/resistance.
### Zone Requirements
- Gap size ≥ 25% of ATR
- Impulse candle has strong body (≥ 70%)
- Impulse candle is 1.5x average range
- Volume above average on impulse
- Created during active session
### Zone States
1. **Fresh** (bright color) - Just created, untested
2. **Tested** (gray) - Price touched zone midpoint
3. **Broken** (removed) - Price closed through zone
### Trading FVG Zones
| Zone | Approach From | Expected |
|------|--------------|----------|
| 🟩 Bull | Above (falling) | Support - look for bounce |
| 🟥 Bear | Below (rising) | Resistance - look for rejection |
---
## 🟣 SINGLE PRINTS
Single prints mark candles with:
- Range > 1.3x average
- Body > 70% of range
- Volume > 1.8x average
- Clear delta dominance
These become horizontal support/resistance lines extending into the future.
---
## 📊 TABLE REFERENCE
| Row | Label | Meaning |
|-----|-------|---------|
| 1 | Pts | Current candle point range |
| 2 | Tier | S/A/B/X classification |
| 3 | Vol | Volume ratio vs 20-bar average |
| 4 | Delta | Buy/Sell percentage dominance |
| 5 | Sess | Current session (LDN/NY/PWR/OFF) |
| 6 | Zone | In FVG zone (BULL/BEAR/---) |
| 7 | Score | Confluence score out of 10 |
| 8 | CVD | Delta momentum direction |
| 9 | R:R | Risk:Reward if signal active |
### Color Coding
- **Green/Lime**: Good, meets threshold
- **Yellow**: Caution, borderline
- **Red**: Bad, below threshold
- **Gray**: Inactive/neutral
---
## 🔧 SETTINGS GUIDE
### Tier Thresholds
| Setting | Default | Notes |
|---------|---------|-------|
| S-Tier | 50 pts | ~$250/contract |
| A-Tier | 25 pts | ~$125/contract |
| B-Tier | 12 pts | ~$60/contract |
### Sniper Filters
| Setting | Default | Notes |
|---------|---------|-------|
| Min Volume Ratio | 1.8x | Lower = more signals |
| Delta Dominance | 62% | Lower = more signals |
| Body Ratio | 70% | Higher = fewer, cleaner |
| Range Multiplier | 1.3x | Higher = fewer, bigger moves |
| CVD Confirm | On | Off = more signals |
### Recommended Configurations
**Conservative (3-4 trades/day):**
```
Min Confluence: 6
Volume Ratio: 2.0
Delta Threshold: 65%
Body Ratio: 75%
```
**Standard (5-7 trades/day):**
```
Min Confluence: 4
Volume Ratio: 1.8
Delta Threshold: 62%
Body Ratio: 70%
```
**Aggressive (7-10 trades/day):**
```
Min Confluence: 3
Volume Ratio: 1.5
Delta Threshold: 60%
Body Ratio: 65%
```
---
## ✓ ENTRY CHECKLIST
Before entering any trade:
1. ☐ Signal present (S🎯, A🎯, B🎯, or Z)
2. ☐ Session active (LDN, NY, or PWR)
3. ☐ Score ≥ 4 (preferably 6+)
4. ☐ Vol shows GREEN
5. ☐ Delta colored (not gray)
6. ☐ CVD arrow matches direction
7. ☐ Note stop/target lines
8. ☐ Execute at signal candle close
---
## ⛔ DO NOT TRADE
- Session shows "OFF"
- Score < 4
- Vol shows RED
- Delta gray (no dominance)
- Multiple conflicting signals
- Major news imminent (FOMC, NFP, CPI)
- Overnight session (11:30 PM - 3:00 AM ET)
---
## 🎯 POSITION SIZING
| Tier | Score | Size | Stop |
|------|-------|------|------|
| S (50+ pts) | 7+ | 100% | Below/above candle |
| A (25-49 pts) | 5-6 | 75% | Below/above candle |
| B (12-24 pts) | 4 | 50% | Below/above candle |
| Zone | Any | 50% | Beyond zone |
---
## 🚨 ALERTS
### Priority Alerts (Set These)
| Alert | Action |
|-------|--------|
| 🎯 S-TIER | Drop everything, check immediately |
| 🎯 A-TIER | Evaluate within 15 seconds |
| 🎯 B-TIER | Check if available |
| 🎯 ZONE | Good context entry |
### Info Alerts (Optional)
| Alert | Purpose |
|-------|---------|
| NEW BULL/BEAR FVG | Mark zones on mental map |
| SINGLE PRINT | Note for future S/R |
| SESSION OPEN | Prepare to trade |
---
## 📈 TRADE JOURNAL
```
DATE: ___________
SESSION: ☐ LDN ☐ NY ☐ PWR
TRADE:
├── Time: _______
├── Signal: S🎯 / A🎯 / B🎯 / Z
├── Direction: LONG / SHORT
├── Score: ___/10
├── Entry: _______
├── Stop: _______
├── Target: _______
├── In Zone: ☐ Yes ☐ No
├── Result: +/- ___ pts ($_____)
└── Notes: _______________________
DAILY:
├── Trades: ___
├── Wins: ___ | Losses: ___
├── Net P/L: $_____
└── Best setup: _______________________
```
---
## 🏆 GOLDEN RULES
> **"Wait for the session. Off-hours = noise."**
> **"Score 6+ is your edge. Anything less is gambling."**
> **"Zone + Tier = bread and butter combo."**
> **"One great trade beats five forced trades."**
> **"Leave every trade with money. YM gives you time."**
---
## 🔧 TROUBLESHOOTING
| Issue | Solution |
|-------|----------|
| No signals | Lower min score to 3-4 |
| Too many signals | Raise min score to 6+ |
| Zones cluttering | Reduce max zones to 8 |
| Missing sessions | Check timezone setting |
| Table not updating | Resize chart or refresh |
---
## 📝 TECHNICAL NOTES
- **Pine Script v6**
- **Works on**: YM, MYM, any Dow futures
- **Recommended TF**: 1-5 minute for day trading
- **Min TradingView Plan**: Free (no intrabar data required)
---
*© Alexandro Disla - YM Ultimate SNIPER v5*
*Clean Build | Proven Components Only*
YM Ultimate SNIPER# YM Ultimate SNIPER - Documentation & Trading Guide
## 🎯 Unified GRA + DeepFlow | YM-Optimized for Low Volatility
**TARGET: 3-7 High-Confluence Trades per Day**
> **Philosophy:** *YM's lower volatility is not a weakness—it's our edge. Predictability + precision = consistent profits.*
---
## ⚡ QUICK REFERENCE CARD
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ YM ULTIMATE SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 💰 YM BASICS: │
│ ═════════════ │
│ • 1 tick = 1 point = $5/contract │
│ • Typical daily range: 150-400 points │
│ • 30-40% less volatile than NQ │
│ • More institutional, less retail noise │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 TIER THRESHOLDS (YM-OPTIMIZED): │
│ ══════════════════════════════════ │
│ S-TIER: 50+ pts = $250+/contract → HOLD (Institutional sweep) │
│ A-TIER: 25-49 pts = $125-245/contract → SWING (Strong momentum) │
│ B-TIER: 12-24 pts = $60-120/contract → SCALP (Quick grab) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⏰ SESSION WINDOWS: │
│ ═══════════════════ │
│ LDN → 3:00-5:00 AM ET (European flow) │
│ NY → 9:30-11:30 AM ET (US opening drive) │
│ PWR → 3:00-4:00 PM ET (End-of-day rebalancing) │
│ │
│ Expected Trades: 1-2 LDN | 2-3 NY | 1-2 PWR = 4-7 total │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 CONFLUENCE SCORING (MAX 10 POINTS): │
│ ═══════════════════════════════════════ │
│ Tier Signal: S=3, A=2, B=1 points │
│ In Active Zone: +2 points │
│ POC Aligned: +1 point (POC at body extreme) │
│ Imbalance Support:+1 point (supporting IMB nearby) │
│ Strong Volume: +1 point (2x+ average) │
│ Strong Delta: +1 point (70%+ dominance) │
│ CVD Momentum: +1 point (CVD trending with signal) │
│ │
│ MINIMUM SCORE: 5/10 to show signal (adjustable) │
│ IDEAL SCORE: 7+/10 for highest probability │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 SIGNAL TYPES: │
│ ═════════════════ │
│ S🎯 / A🎯 / B🎯 → GRA Tier Signals (Full confluence) │
│ Z🎯 → Zone Entry (At DFZ zone + delta + volume) │
│ SP → Single Print (Institutional impulse) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ✓ ENTRY CHECKLIST: │
│ ═══════════════════ │
│ □ Signal appears (check Score ≥5) │
│ □ Session active (LDN!/NY!/PWR!) │
│ □ Table: Vol GREEN, Delta colored, Body GREEN │
│ □ CVD arrow (▲/▼) matches direction │
│ □ Note stop/target lines on chart │
│ □ Check Zone status (bonus if IN ZONE) │
│ □ Execute at signal candle close │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 POSITION SIZING BY TIER: │
│ ═══════════════════════════ │
│ S-TIER (50+ pts): Full size, hold 2-5 min, target 2.5:1 R:R │
│ A-TIER (25-49): 75% size, hold 1-3 min, target 2.0:1 R:R │
│ B-TIER (12-24): 50% size, hold 30-90 sec, target 1.5:1 R:R │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ DO NOT TRADE WHEN: │
│ ════════════════════ │
│ ✗ Session shows "---" │
│ ✗ Score < 5/10 │
│ ✗ Vol shows RED (<1.8x) │
│ ✗ Delta < 62% │
│ ✗ Multiple conflicting signals │
│ ✗ Just before major news (FOMC, NFP, etc.) │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 WHY YM? LEVERAGING LOW VOLATILITY
### The YM Advantage
Most traders avoid YM because "it doesn't move enough." This is precisely why it's perfect for precision scalping:
| Factor | NQ | YM | Advantage |
|--------|----|----|-----------|
| **Daily Range** | 300-600 pts | 150-400 pts | More predictable moves |
| **Tick Value** | $5/tick (4 ticks/pt) | $5/tick (1 tick/pt) | Simpler math |
| **Retail Noise** | High | Low | Cleaner signals |
| **Whipsaws** | Frequent | Rare | Fewer fakeouts |
| **Trend Persistence** | Short | Long | Easier holds |
| **Fill Quality** | Variable | Consistent | Better execution |
### Why 3-7 Trades is the Sweet Spot
```
YM SESSION BREAKDOWN:
════════════════════
LONDON (3-5 AM ET): 1-2 trades
├── Why: European institutions positioning for US open
├── Character: Slow build-up, clean trends
└── Best signals: Zone entries + A/B tier
NY OPEN (9:30-11:30 AM ET): 2-3 trades
├── Why: Highest volume, most institutional activity
├── Character: Initial balance formation, breakouts
└── Best signals: S/A tier, zone confluence
POWER HOUR (3-4 PM ET): 1-2 trades
├── Why: End-of-day rebalancing, MOC orders
├── Character: Mean reversion or trend acceleration
└── Best signals: Zone entries, B tier quick scalps
TOTAL: 4-7 high-quality setups per day
```
---
## 🔧 YM-SPECIFIC OPTIMIZATIONS
This unified indicator has been specifically tuned for YM's characteristics:
### Tier Thresholds
| Tier | NQ (Original) | YM (Optimized) | Rationale |
|------|---------------|----------------|-----------|
| S-Tier | 100 pts | **50 pts** | YM's daily range is ~50% of NQ |
| A-Tier | 50 pts | **25 pts** | Proportional scaling |
| B-Tier | 20 pts | **12 pts** | Still 5%+ of typical daily range |
### Filter Adjustments
| Filter | NQ Value | YM Value | Why |
|--------|----------|----------|-----|
| Volume Ratio | 1.5x | **1.8x** | Higher bar = less retail noise |
| Delta Threshold | 60% | **62%** | Tighter for cleaner signals |
| Body Ratio | 70% | **72%** | More conviction required |
| Range Multiplier | 1.3x | **1.4x** | Bigger move = real signal |
| Gap ATR% | 30% | **25%** | Smaller gaps still significant |
| Zone Age | 50 bars | **75 bars** | Zones last longer in slow market |
### Why These Changes Work
1. **Higher Volume Bar**: YM has more institutional flow. Requiring 1.8x volume ensures we're catching real moves, not retail chop.
2. **Tighter Delta**: With less noise, we can demand clearer buyer/seller dominance before entering.
3. **Longer Zone Life**: YM trends persist longer. A zone that would be stale in NQ is still viable in YM.
4. **Smaller Gap Threshold**: YM gaps are naturally smaller. 25% of ATR in YM is significant institutional activity.
---
## 📊 CONFLUENCE SCORING SYSTEM
The unified indicator uses a 10-point confluence scoring system to filter for only the highest-probability setups:
### Score Breakdown
```
CONFLUENCE SCORE CALCULATION:
═════════════════════════════
BASE POINTS (Tier):
├── S-Tier signal: +3 points
├── A-Tier signal: +2 points
└── B-Tier signal: +1 point
BONUS POINTS:
├── Inside Active Zone (DFZ): +2 points
│ └── Price within bull/bear zone = institutional level
│
├── POC Alignment: +1 point
│ └── POC at body extreme = strong conviction
│
├── Imbalance Support: +1 point
│ └── Supporting imbalance within 1 ATR
│
├── Strong Volume (2x+): +1 point
│ └── Exceptional institutional participation
│
├── Strong Delta (70%+): +1 point
│ └── Clear one-sided aggression
│
└── CVD Momentum: +1 point
└── CVD trending with signal direction
MAXIMUM POSSIBLE: 10 points
```
### Score Interpretation
| Score | Quality | Action | Expected Win Rate |
|-------|---------|--------|-------------------|
| 8-10 | 🥇 Elite | Full size, hold for target | 75-80% |
| 6-7 | 🥈 Strong | Standard size, manage actively | 65-70% |
| 5 | 🥉 Valid | Reduced size, quick scalp | 55-60% |
| <5 | ⚫ Filtered | No signal shown | N/A |
### Adjusting Minimum Score
- **Conservative (Score ≥6)**: Fewer trades, higher win rate
- **Standard (Score ≥5)**: Balanced approach, 3-7 trades/day
- **Aggressive (Score ≥4)**: More trades, requires active management
---
## 📐 SIGNAL TYPES EXPLAINED
### 1. GRA Tier Signals (S🎯, A🎯, B🎯)
These are the primary signals from the merged GRA system:
```
TIER SIGNAL REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Point movement meets tier threshold
├── ✓ Volume ≥ 1.8x average
├── ✓ Delta ≥ 62% (buy or sell dominance)
├── ✓ Body ≥ 72% of candle range
├── ✓ Range ≥ 1.4x average
├── ✓ Small opposite wick (<50% of body)
├── ✓ CVD confirms direction (if enabled)
├── ✓ Active session (LDN/NY/PWR)
└── ✓ Confluence Score ≥ minimum (default 5)
```
### 2. Zone Entry Signals (Z🎯)
When price enters a DeepFlow zone with confirmation:
```
ZONE ENTRY REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Price inside fresh/tested zone (not broken)
├── ✓ Delta ≥ 62% in zone direction
├── ✓ Volume ≥ 1.5x average
└── ✓ Active session
NOTE: Z🎯 only appears when NOT already showing tier signal
(prevents duplicate signals on same candle)
```
### 3. Single Print Markers (SP)
Mark institutional impulse candles for future S/R:
```
SINGLE PRINT REQUIREMENTS:
═══════════════════════════
ALL must be TRUE:
├── ✓ Range ≥ 1.6x average
├── ✓ Body ≥ 72% of range
├── ✓ Volume ≥ 1.8x average
├── ✓ Delta ≥ 62% confirms direction
└── ✓ Active session
USE: Horizontal lines at high/low act as future S/R
```
---
## 🎯 TRADING STRATEGIES
### Strategy 1: Zone + Tier Confluence (Highest Probability)
```
THE ULTIMATE YM SETUP:
═══════════════════════
Setup:
1. Active DeepFlow zone exists (green box below for long)
2. Price pulls back INTO the zone
3. Tier signal fires INSIDE the zone (S🎯/A🎯)
4. Score shows 7+/10
Entry: Signal candle close
Stop: Below zone bottom (for longs)
Target: Based on tier (1.5-2.5:1 R:R)
Why It Works:
• Zone = institutional limit orders
• Tier signal = momentum confirmation
• Double confirmation = high probability
Expected Win Rate: 70-75%
```
### Strategy 2: Pure Tier Signal with POC Stop
```
SNIPER TIER TRADE:
══════════════════
Setup:
1. Tier signal appears (preferably A or S)
2. Score ≥ 5/10
3. Note POC level on signal candle
4. Red/green stop/target lines appear
Entry: Signal candle close
Stop: Beyond POC (shown on chart)
Target: Auto-calculated based on tier
Key: POC placement matters
• POC near candle bottom (longs) = STRONG
• POC in middle = weaker signal
• POC at extreme = possible exhaustion
Expected Win Rate: 60-65%
```
### Strategy 3: Zone Bounce (Continuation)
```
ZONE BOUNCE TRADE:
══════════════════
Setup:
1. Fresh zone created during session
2. Price leaves zone, moves in zone direction
3. Price returns to test zone (within 15 bars)
4. Z🎯 signal appears or rejection candle forms
Entry: At CE line (middle of zone)
Stop: Beyond zone edge
Target: Previous swing high/low
Why It Works:
• Zones represent unfilled orders
• First retest often finds support/resistance
• Lower volatility = cleaner bounces
Expected Win Rate: 55-60%
```
### Strategy 4: Single Print Scalp
```
SINGLE PRINT SCALP:
═══════════════════
Setup:
1. Single Print (SP) marker appears
2. Note the gold/purple lines at high/low
3. Wait for price to return to SP level
4. Look for rejection or tier signal at level
Entry: At SP line with confirmation
Stop: Beyond the SP line
Target: Quick 1:1 or to next structure
Why It Works:
• SP = price moved too fast, orders unfilled
• Price often returns to "fill" these levels
• YM's slower pace makes retests likely
Expected Win Rate: 55-60%
```
---
## 📊 TABLE LEGEND
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Pts** | Current candle points | Gold/Green/Yellow = Tiered |
| **Tier** | S/A/B/X | Tier color or white |
| **Vol** | Volume ratio | 🟢 ≥1.8x, 🔴 <1.8x |
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom |
| **Body** | Body % of range | 🟢 ≥72%, 🔴 <72% |
| **CVD** | Trend direction | ▲ Bullish, ▼ Bearish |
| **Sess** | Active session | 🟡 LDN!/NY!/PWR!, ⚫ --- |
| **POC** | Point of Control | 🟡 Gold price level |
| **Zone** | Zone position | 🟢 BUY⬚, 🔴 SELL⬚, ⚫ --- |
| **Zones** | Active zone count | #B/#S format |
| **Score** | Confluence score | 🟢 7+, 🟡 5-6, ⚫ <5 |
| **IMB** | Recent imbalances | Count in last 10 bars |
| **R:R** | Risk/Reward | 🟢 On signal, ⚫ No signal |
---
## ⏰ SESSION-SPECIFIC PLAYBOOKS
### London Session (3:00-5:00 AM ET)
```
CHARACTER: Slow, methodical, trend-building
VOLUME: Medium (50-70% of NY)
BEST SETUPS: Zone entries, A/B tier with zones
PLAYBOOK:
• Enter on zone retests
• Expect 15-25 pt moves
• Don't fight early direction
• Watch for pre-NY positioning
TYPICAL TRADES: 1-2
```
### NY Open (9:30-11:30 AM ET)
```
CHARACTER: Fast, volatile, high-conviction
VOLUME: Highest of day
BEST SETUPS: S/A tier, zone confluence
PLAYBOOK:
• First 15 min: Observe Initial Balance
• 9:45-10:15: Best setups form
• S-tier signals = ride the wave
• Be aggressive on high scores
TYPICAL TRADES: 2-3
```
### Power Hour (3:00-4:00 PM ET)
```
CHARACTER: Rebalancing, MOC orders
VOLUME: Medium-high (70-80% of NY)
BEST SETUPS: B tier scalps, zone entries
PLAYBOOK:
• Watch for mean reversion setups
• Quick scalps around POC levels
• Don't hold through close
• Take profits at 1:1 R:R
TYPICAL TRADES: 1-2
```
---
## 🔧 RECOMMENDED SETTINGS
### Conservative (Fewer, Better Trades)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 6 | Only strong setups |
| Min Volume Ratio | 2.0 | Higher bar |
| Delta Threshold | 65% | Stricter dominance |
| Max Zones | 8 | Less clutter |
### Standard (Balanced)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 5 | Default |
| Min Volume Ratio | 1.8 | Default |
| Delta Threshold | 62% | Default |
| Max Zones | 12 | Default |
### Aggressive (More Opportunities)
| Setting | Value | Notes |
|---------|-------|-------|
| Min Confluence Score | 4 | More signals |
| Min Volume Ratio | 1.5 | Lower bar |
| Delta Threshold | 60% | Looser |
| Max Zones | 15 | More context |
---
## 🚨 ALERT SETUP
Configure these alerts in TradingView:
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 YM S-TIER LONG/SHORT | 🔴 CRITICAL | Drop everything, check immediately |
| 🎯 YM A-TIER LONG/SHORT | 🟠 HIGH | Evaluate within 15 seconds |
| 🎯 YM B-TIER LONG/SHORT | 🟡 MEDIUM | Check if available |
| 🎯 YM ZONE BUY/SELL | 🟢 STANDARD | Good context entry |
| 📦 NEW ZONE | 🔵 INFO | Mark on mental map |
| ⭐ SINGLE PRINT | 🔵 INFO | Note for future S/R |
| SESSION OPEN | ⚪ INFO | Prepare to trade |
### Alert Message Format
```
🎯 YM A-LONG | YM1! @ 42,150 | 68%B | Score: 7/10 | IN ZONE | POC: 42,125 | Stop: 42,098 | SWING
```
---
## ⚠️ COMMON MISTAKES TO AVOID
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading outside sessions | Low volume = noise | Wait for LDN/NY/PWR |
| Ignoring score | Low scores = low probability | Require ≥5/10 |
| Fighting the zone | Zones are institutional | Trade WITH zones |
| Oversizing B-tier | Quick scalps, not holds | 50% size max |
| Holding through news | Volatility spike | Exit before FOMC, NFP |
| Chasing after signal | Entry on close only | Miss it = wait for next |
| Ignoring POC position | Middle POC = indecision | Strong = extreme POC |
---
## 📈 DAILY TRADE JOURNAL TEMPLATE
```
DATE: ___________
SESSION: □ LDN □ NY □ PWR
TRADE 1:
├── Time: _______
├── Signal: S🎯 / A🎯 / B🎯 / Z🎯
├── Score: ___/10
├── Entry: _______
├── Stop: _______
├── Target: _______
├── In Zone: □ Yes □ No
├── Result: +/- ___ pts ($_____)
└── Notes: _______________________
TRADE 2:
DAILY SUMMARY:
├── Total Trades: ___
├── Win Rate: ___%
├── Net P/L: $_____
├── Best Setup: _______
└── Improvement: _______________________
```
---
## 🏆 GOLDEN RULES FOR YM
> **"YM rewards patience. Wait for the confluence—it's worth it."**
> **"Low volatility means you can size up. One good trade beats five forced trades."**
> **"Score 7+ is your edge. Anything less is gambling."**
> **"The zone + tier combo is your bread and butter. Master it."**
> **"Leave every trade with money. YM gives you time to manage."**
---
## 📊 VISUAL GUIDE
```
PERFECT YM SNIPER SETUP:
═══════════════════════════════════════════════════════════════════
│ Current Price
│
┌─────────────────────────┴────────────────────────────┐
│ BEARISH ZONE (Red) │
│- - - - - - - CE Line (Entry for shorts) - - - - - - │
│ │
└──────────────────────────────────────────────────────┘
│
══════════════════╪══════════════════ SP High (Purple)
│
┌─────────────────────┤
│█████████████████████│ ← A🎯 LONG Signal
│█████████████████████│ Score: 8/10
│ ●──────────────────│ ← POC (Gold) near bottom = STRONG
│█████████████████████│
│█████████████████████│
└─────────────────────┤
│
══════════════════╪══════════════════ SP Low (Purple)
│
┌─────────────────────────┴────────────────────────────┐
│ BULLISH ZONE (Green) │
│- - - - - - - CE Line (Entry for longs) - - - - - - -│
│██████████████████████████████████████████████████████│
└──────────────────────────────────────────────────────┘
│
Stop Loss
CONFLUENCE CHECK:
✓ A-Tier signal (+2)
✓ At edge of bullish zone (+2)
✓ POC at bottom of candle (+1)
✓ Strong volume 2.3x (+1)
✓ Delta 72% buyers (+1)
✓ CVD bullish (+1)
TOTAL: 8/10 = ELITE SETUP
ACTION: Full size LONG at signal candle close
STOP: Below zone bottom
TARGET: 2:1 R:R (auto-calculated)
```
---
## 🔧 TROUBLESHOOTING
| Issue | Cause | Fix |
|-------|-------|-----|
| No signals appearing | Score too high | Lower min score to 4-5 |
| Too many signals | Score too low | Raise min score to 6+ |
| Zones cluttering chart | Max zones high | Reduce to 8-10 |
| POC not showing | Tiered filter on | Check "POC Only Tiered" |
| Session not highlighting | Wrong timezone | Verify timezone setting |
| Alerts not firing | Not configured | Set up in TradingView alerts |
---
## 📝 PINE SCRIPT V6 TECHNICAL NOTES
This indicator uses advanced features:
- **User Defined Types (UDT)**: Clean state management for zones/imbalances
- **`request.security_lower_tf()`**: Intrabar volume analysis
- **Dynamic Array Management**: Efficient memory for drawings
- **Confluence Scoring Engine**: Multi-factor signal qualification
- **Auto Stop/Target**: Dynamic risk management calculation
**Minimum TradingView Plan:** Pro (for intrabar data access)
---
*© Alexandro Disla - YM Ultimate SNIPER*
*Pine Script v6 | TradingView*
*Unified GRA v5 + DeepFlow Zones | YM-Optimized*
Dynamic SMA Trend System [Multi-Stage Risk Engine]Description:
This script implements a robust Trend Following strategy based on a multiple Simple Moving Average (SMA) crossover logic (25, 50, 100, 200). What sets this strategy apart is its advanced "4-Stage Risk Engine" and a smart "High-Water Mark" Re-Entry system, designed to protect profits during parabolic moves while filtering out chop during sideways markets.
How it works:
The strategy operates on three core pillars: Trend Identification, Dynamic Risk Management, and Momentum Re-Entry.
1. Entry Logic (Trend Identification) The script looks for crossovers at different trend stages to capture early reversals as well as established trends:
Short-Term: SMA 25 crosses over SMA 50.
Mid-Term: SMA 50 crosses over SMA 100.
Macro-Trend: SMA 100 crosses over SMA 200.
2. The 4-Stage Risk Engine (Dynamic Stop Loss) Instead of a static Stop Loss, this strategy uses a progressive system that adapts as the price increases:
Stage 1 (Protection): Starts with a fixed Stop Loss (default -10%) to give the trade room to breathe.
Stage 2 (Break-Even): Once the price rises by 12%, the Stop is moved to trailing mode (10% distance), effectively securing a near break-even state.
Stage 3 (Profit Locking): At 25% profit, the trailing stop tightens to 8% to lock in gains.
Stage 4 (Parabolic Mode): At 40% profit, the trailing stop tightens further to 5% to capture the peak of parabolic moves.
3. Dual Exit Mechanism The strategy exits a position if EITHER of the following happens:
Stop Loss Hit: Price falls below the dynamic red line (Risk Engine).
Dead Cross: The trend structure breaks (e.g., SMA 25 crosses under SMA 50), signaling a momentum loss even if the Stop Loss wasn't hit.
4. "High-Water Mark" Re-Entry To avoid "whipsaws" in choppy markets, the script does not re-enter immediately after a stop-out.
It marks the highest price of the previous trade (Green Dotted Line).
A Re-Entry only occurs if the price breaks above this previous high (showing renewed strength) AND the long-term trend is bullish (Price > SMA 200).
Visuals:
SMAs: 25 (Yellow), 50 (Orange), 100 (Blue), 200 (White).
Red Line: Visualizes the dynamic Stop Loss level.
Green Dots: Visualizes the target price needed for a valid re-entry.
Settings: All parameters (SMA lengths, Stop Loss percentages, Staging triggers) are fully customizable in the settings menu to fit different assets (Crypto, Stocks, Forex) and timeframes.
MTC – Multi-Timeframe Trend Confirmator V2MTC – Multi-Timeframe Trend Confirmator V2
A comprehensive trend analysis indicator that systematically combines six technical indicators across three customizable timeframes, using a weighted scoring system to identify high-probability trend conditions.
ORIGINALITY AND CONCEPT
This indicator is original in its approach to multi-timeframe trend confirmation. Rather than relying on a single indicator or timeframe, it creates a composite score by evaluating six different technical conditions simultaneously across three timeframes. The scoring system weighs certain indicators more heavily based on their reliability in trend identification. The visual gauge provides an at-a-glance view of trend alignment across timeframes, making it easier to identify when multiple timeframes agree - a condition that typically produces stronger, more reliable trends.
HOW IT WORKS - DETAILED SCORING METHODOLOGY
The indicator evaluates six technical conditions on each timeframe. Each condition contributes to a composite score:
EMA 200 (Weight: 1 point)
Bullish: Price closes above EMA 200 (+1)
Bearish: Price closes below EMA 200 (-1)
Rationale: Long-term trend direction
SMA 50/200 Crossover (Weight: 1 point)
Bullish: SMA 50 above SMA 200 (+1)
Bearish: SMA 50 below SMA 200 (-1)
Rationale: Golden/Death cross confirmation
RSI 14 (Weight: 1 point)
Bullish: RSI above 55 (+1)
Bearish: RSI below 45 (-1)
Neutral: RSI between 45-55 (0)
Rationale: Momentum filter with buffer zone to avoid chop
MACD (12,26,9) (Weight: 1 point)
Bullish: MACD line above signal line (+1)
Bearish: MACD line below signal line (-1)
Rationale: Trend momentum confirmation
ADX 14 (Weight: 2 points - DOUBLE WEIGHTED)
Requires ADX above 25 to activate
Bullish: DI+ above DI- and ADX > 25 (+2)
Bearish: DI- above DI+ and ADX > 25 (-2)
Neutral: ADX below 25 (0)
Rationale: Trend strength filter - only counts when a strong trend exists. Double weighted because ADX is specifically designed to measure trend strength, making it more reliable than oscillators.
Supertrend (Factor: 3.0, ATR Period: 10) (Weight: 2 points - DOUBLE WEIGHTED)
Bullish: Direction indicator = -1 (+2)
Bearish: Direction indicator = +1 (-2)
Rationale: Dynamic support/resistance that adapts to volatility. Double weighted because Supertrend provides clear, objective trend signals with built-in stop-loss levels.
COMPOSITE SCORE CALCULATION:
Total possible score range: -10 to +10 points
Score interpretation:
Score > 2: UPTREND (majority of indicators bullish, especially weighted ones)
Score < -2: DOWNTREND (majority of indicators bearish, especially weighted ones)
Score between -2 and +2: NEUTRAL/RANGING (mixed signals or weak trend)
The threshold of +/- 2 was chosen because it requires more than just basic agreement - it typically means at least 3-4 indicators align, or that the heavily-weighted indicators (ADX, Supertrend) confirm the direction.
MULTI-TIMEFRAME LOGIC:
The indicator calculates the composite score independently for three timeframes:
Higher Timeframe (default: 4H) - Major trend direction
Mid Timeframe (default: 1H) - Intermediate trend
Lower Timeframe (default: 15min) - Entry timing
Main Trend Confirmation Rule:
The indicator only signals a confirmed trend when BOTH the higher timeframe AND mid timeframe scores agree (both > 2 for uptrend, or both < -2 for downtrend). This dual-timeframe confirmation significantly reduces false signals during choppy or ranging markets.
HOW TO USE IT
Setup:
Add indicator to chart
Customize timeframes based on your trading style:
Scalpers: 15min, 5min, 1min
Day traders: 4H, 1H, 15min (default)
Swing traders: Daily, 4H, 1H
Toggle individual indicators on/off based on your preference
Adjust Supertrend parameters if needed for your instrument's volatility
Reading the Gauge (Top Right Corner):
Each row shows one timeframe
Left column: Timeframe label
Middle column: Visual strength bars (10 bars = maximum score)
Green bars = Bullish score
Red bars = Bearish score
Yellow bars = Neutral/ranging
More filled bars = stronger trend
Right column: Numerical score
Trading Signals:
Entry Signals:
Long Entry: Wait for upward triangle arrow (appears when higher + mid TF both bullish)
Confirm gauge shows green bars on higher and mid timeframes
Lower timeframe should ideally turn green for entry timing
Chart background tints light green
Short Entry: Wait for downward triangle arrow (appears when higher + mid TF both bearish)
Confirm gauge shows red bars on higher and mid timeframes
Lower timeframe should ideally turn red for entry timing
Chart background tints light red
Position Management:
Stay in position while higher and mid timeframes remain aligned
Consider reducing position size when mid timeframe score weakens
Exit when higher timeframe trend reverses (daily label changes)
Avoiding False Signals:
Ignore signals when gauge shows mixed colors across timeframes
Avoid trading when scores are close to threshold (+/- 2 to +/- 4 range)
Best trades occur when all three timeframes align (all green or all red in gauge)
Use the numerical scores: higher absolute values (7-10) indicate stronger, more reliable trends
Practical Examples:
Example 1 - Strong Uptrend Entry:
Higher TF: +8 (strong green bars)
Mid TF: +6 (strong green bars)
Lower TF: +4 (moderate green bars)
Action: Look for long entries on lower timeframe pullbacks
Background is tinted green, upward arrow appears
Example 2 - Ranging Market (Avoid):
Higher TF: +3 (weak green)
Mid TF: -1 (weak red)
Lower TF: +2 (neutral yellow)
Action: Stay out, wait for alignment
Example 3 - Trend Reversal Warning:
Higher TF: +7 (still green)
Mid TF: -3 (turned red)
Lower TF: -5 (strong red)
Action: Consider exiting longs, prepare for potential higher TF reversal
Customization Options:
Timeframes: Adjust all three to match your trading horizon
Indicator Toggles: Disable indicators that don't suit your instrument:
Disable RSI for highly volatile crypto markets
Disable SMA crossover for range-bound instruments
Keep ADX and Supertrend enabled for trending markets
Visual Preferences:
Arrow size: 5 options from Tiny to Huge
Gauge size: Small/Medium/Large for different screen sizes
Toggle arrows on/off if you only want the gauge
Alert Setup:
Right-click chart, "Add Alert"
Condition: MTC v6 - UPTREND or DOWNTREND
Get notified when multi-timeframe confirmation occurs
Best Practices:
Use with Price Action: The indicator works best when combined with support/resistance levels, chart patterns, and volume analysis
Risk Management: Even with multi-timeframe confirmation, always use stop losses
Market Context: Works best in trending markets; less reliable in strong consolidation
Backtesting: Test the default settings on your specific instrument and timeframe before live trading
Patience: Wait for full multi-timeframe alignment rather than taking premature signals
Technical Notes:
All calculations use Pine Script's security function to fetch data from multiple timeframes
Prevents repainting by using confirmed bar data
Gauge updates in real-time on the last bar
Daily labels mark at the open of each new daily candle
Works on all instruments and timeframes
This indicator is ideal for traders who want objective, systematic trend identification without the complexity of analyzing multiple indicators manually across different timeframes.
-NATANTIA
TenUp Bots S R - Fixed (ta.highest)//@version=5
indicator("TenUp Bots S R - Fixed (ta.highest)", overlay = true)
// Inputs
a = input.int(10, "Sensitivity (bars)", minval = 1, maxval = 9999)
d_pct = input.int(85, "Transparency (%)", minval = 0, maxval = 100)
// Convert 0-100% to 0-255 transparency (color.new uses 0..255)
transp = math.round(d_pct * 255 / 100)
// Colors with transparency applied
resColor = color.new(color.red, transp)
supColor = color.new(color.blue, transp)
// Helper (calculations only)
getRes(len) => ta.highest(high, len)
getSup(len) => ta.lowest(low, len)
// === PLOTS (all in global scope) ===
plot(getRes(a*1), title="Resistance 1", color=resColor, linewidth=2)
plot(getSup(a*1), title="Support 1", color=supColor, linewidth=2)
plot(getRes(a*2), title="Resistance 2", color=resColor, linewidth=2)
plot(getSup(a*2), title="Support 2", color=supColor, linewidth=2)
plot(getRes(a*3), title="Resistance 3", color=resColor, linewidth=2)
plot(getSup(a*3), title="Support 3", color=supColor, linewidth=2)
plot(getRes(a*4), title="Resistance 4", color=resColor, linewidth=2)
plot(getSup(a*4), title="Support 4", color=supColor, linewidth=2)
plot(getRes(a*5), title="Resistance 5", color=resColor, linewidth=2)
plot(getSup(a*5), title="Support 5", color=supColor, linewidth=2)
plot(getRes(a*6), title="Resistance 6", color=resColor, linewidth=2)
plot(getSup(a*6), title="Support 6", color=supColor, linewidth=2)
plot(getRes(a*7), title="Resistance 7", color=resColor, linewidth=2)
plot(getSup(a*7), title="Support 7", color=supColor, linewidth=2)
plot(getRes(a*8), title="Resistance 8", color=resColor, linewidth=2)
plot(getSup(a*8), title="Support 8", color=supColor, linewidth=2)
plot(getRes(a*9), title="Resistance 9", color=resColor, linewidth=2)
plot(getSup(a*9), title="Support 9", color=supColor, linewidth=2)
plot(getRes(a*10), title="Resistance 10", color=resColor, linewidth=2)
plot(getSup(a*10), title="Support 10", color=supColor, linewidth=2)
plot(getRes(a*15), title="Resistance 15", color=resColor, linewidth=2)
plot(getSup(a*15), title="Support 15", color=supColor, linewidth=2)
plot(getRes(a*20), title="Resistance 20", color=resColor, linewidth=2)
plot(getSup(a*20), title="Support 20", color=supColor, linewidth=2)
plot(getRes(a*25), title="Resistance 25", color=resColor, linewidth=2)
plot(getSup(a*25), title="Support 25", color=supColor, linewidth=2)
plot(getRes(a*30), title="Resistance 30", color=resColor, linewidth=2)
plot(getSup(a*30), title="Support 30", color=supColor, linewidth=2)
plot(getRes(a*35), title="Resistance 35", color=resColor, linewidth=2)
plot(getSup(a*35), title="Support 35", color=supColor, linewidth=2)
plot(getRes(a*40), title="Resistance 40", color=resColor, linewidth=2)
plot(getSup(a*40), title="Support 40", color=supColor, linewidth=2)
plot(getRes(a*45), title="Resistance 45", color=resColor, linewidth=2)
plot(getSup(a*45), title="Support 45", color=supColor, linewidth=2)
plot(getRes(a*50), title="Resistance 50", color=resColor, linewidth=2)
plot(getSup(a*50), title="Support 50", color=supColor, linewidth=2)
plot(getRes(a*75), title="Resistance 75", color=resColor, linewidth=2)
plot(getSup(a*75), title="Support 75", color=supColor, linewidth=2)
plot(getRes(a*100), title="Resistance 100", color=resColor, linewidth=2)
plot(getSup(a*100), title="Support 100", color=supColor, linewidth=2)
plot(getRes(a*150), title="Resistance 150", color=resColor, linewidth=2)
plot(getSup(a*150), title="Support 150", color=supColor, linewidth=2)
plot(getRes(a*200), title="Resistance 200", color=resColor, linewidth=2)
plot(getSup(a*200), title="Support 200", color=supColor, linewidth=2)
plot(getRes(a*250), title="Resistance 250", color=resColor, linewidth=2)
plot(getSup(a*250), title="Support 250", color=supColor, linewidth=2)
plot(getRes(a*300), title="Resistance 300", color=resColor, linewidth=2)
plot(getSup(a*300), title="Support 300", color=supColor, linewidth=2)
plot(getRes(a*350), title="Resistance 350", color=resColor, linewidth=2)
plot(getSup(a*350), title="Support 350", color=supColor, linewidth=2)
plot(getRes(a*400), title="Resistance 400", color=resColor, linewidth=2)
plot(getSup(a*400), title="Support 400", color=supColor, linewidth=2)
plot(getRes(a*450), title="Resistance 450", color=resColor, linewidth=2)
plot(getSup(a*450), title="Support 450", color=supColor, linewidth=2)
plot(getRes(a*500), title="Resistance 500", color=resColor, linewidth=2)
plot(getSup(a*500), title="Support 500", color=supColor, linewidth=2)
plot(getRes(a*750), title="Resistance 750", color=resColor, linewidth=2)
plot(getSup(a*750), title="Support 750", color=supColor, linewidth=2)
plot(getRes(a*1000), title="Resistance 1000", color=resColor, linewidth=2)
plot(getSup(a*1000), title="Support 1000", color=supColor, linewidth=2)
plot(getRes(a*1250), title="Resistance 1250", color=resColor, linewidth=2)
plot(getSup(a*1250), title="Support 1250", color=supColor, linewidth=2)
plot(getRes(a*1500), title="Resistance 1500", color=resColor, linewidth=2)
plot(getSup(a*1500), title="Support 1500", color=supColor, linewidth=2)
TenUp Bots S R - Fixed (ta.highest)//@version=5
indicator("TenUp Bots S R - Fixed (ta.highest)", overlay = true)
// Inputs
a = input.int(10, "Sensitivity (bars)", minval = 1, maxval = 9999)
d_pct = input.int(85, "Transparency (%)", minval = 0, maxval = 100)
// Convert 0-100% to 0-255 transparency (color.new uses 0..255)
transp = math.round(d_pct * 255 / 100)
// Colors with transparency applied
resColor = color.new(color.red, transp)
supColor = color.new(color.blue, transp)
// Helper (calculations only)
getRes(len) => ta.highest(high, len)
getSup(len) => ta.lowest(low, len)
// === PLOTS (all in global scope) ===
plot(getRes(a*1), title="Resistance 1", color=resColor, linewidth=2)
plot(getSup(a*1), title="Support 1", color=supColor, linewidth=2)
plot(getRes(a*2), title="Resistance 2", color=resColor, linewidth=2)
plot(getSup(a*2), title="Support 2", color=supColor, linewidth=2)
plot(getRes(a*3), title="Resistance 3", color=resColor, linewidth=2)
plot(getSup(a*3), title="Support 3", color=supColor, linewidth=2)
plot(getRes(a*4), title="Resistance 4", color=resColor, linewidth=2)
plot(getSup(a*4), title="Support 4", color=supColor, linewidth=2)
plot(getRes(a*5), title="Resistance 5", color=resColor, linewidth=2)
plot(getSup(a*5), title="Support 5", color=supColor, linewidth=2)
plot(getRes(a*6), title="Resistance 6", color=resColor, linewidth=2)
plot(getSup(a*6), title="Support 6", color=supColor, linewidth=2)
plot(getRes(a*7), title="Resistance 7", color=resColor, linewidth=2)
plot(getSup(a*7), title="Support 7", color=supColor, linewidth=2)
plot(getRes(a*8), title="Resistance 8", color=resColor, linewidth=2)
plot(getSup(a*8), title="Support 8", color=supColor, linewidth=2)
plot(getRes(a*9), title="Resistance 9", color=resColor, linewidth=2)
plot(getSup(a*9), title="Support 9", color=supColor, linewidth=2)
plot(getRes(a*10), title="Resistance 10", color=resColor, linewidth=2)
plot(getSup(a*10), title="Support 10", color=supColor, linewidth=2)
plot(getRes(a*15), title="Resistance 15", color=resColor, linewidth=2)
plot(getSup(a*15), title="Support 15", color=supColor, linewidth=2)
plot(getRes(a*20), title="Resistance 20", color=resColor, linewidth=2)
plot(getSup(a*20), title="Support 20", color=supColor, linewidth=2)
plot(getRes(a*25), title="Resistance 25", color=resColor, linewidth=2)
plot(getSup(a*25), title="Support 25", color=supColor, linewidth=2)
plot(getRes(a*30), title="Resistance 30", color=resColor, linewidth=2)
plot(getSup(a*30), title="Support 30", color=supColor, linewidth=2)
plot(getRes(a*35), title="Resistance 35", color=resColor, linewidth=2)
plot(getSup(a*35), title="Support 35", color=supColor, linewidth=2)
plot(getRes(a*40), title="Resistance 40", color=resColor, linewidth=2)
plot(getSup(a*40), title="Support 40", color=supColor, linewidth=2)
plot(getRes(a*45), title="Resistance 45", color=resColor, linewidth=2)
plot(getSup(a*45), title="Support 45", color=supColor, linewidth=2)
plot(getRes(a*50), title="Resistance 50", color=resColor, linewidth=2)
plot(getSup(a*50), title="Support 50", color=supColor, linewidth=2)
plot(getRes(a*75), title="Resistance 75", color=resColor, linewidth=2)
plot(getSup(a*75), title="Support 75", color=supColor, linewidth=2)
plot(getRes(a*100), title="Resistance 100", color=resColor, linewidth=2)
plot(getSup(a*100), title="Support 100", color=supColor, linewidth=2)
plot(getRes(a*150), title="Resistance 150", color=resColor, linewidth=2)
plot(getSup(a*150), title="Support 150", color=supColor, linewidth=2)
plot(getRes(a*200), title="Resistance 200", color=resColor, linewidth=2)
plot(getSup(a*200), title="Support 200", color=supColor, linewidth=2)
plot(getRes(a*250), title="Resistance 250", color=resColor, linewidth=2)
plot(getSup(a*250), title="Support 250", color=supColor, linewidth=2)
plot(getRes(a*300), title="Resistance 300", color=resColor, linewidth=2)
plot(getSup(a*300), title="Support 300", color=supColor, linewidth=2)
plot(getRes(a*350), title="Resistance 350", color=resColor, linewidth=2)
plot(getSup(a*350), title="Support 350", color=supColor, linewidth=2)
plot(getRes(a*400), title="Resistance 400", color=resColor, linewidth=2)
plot(getSup(a*400), title="Support 400", color=supColor, linewidth=2)
plot(getRes(a*450), title="Resistance 450", color=resColor, linewidth=2)
plot(getSup(a*450), title="Support 450", color=supColor, linewidth=2)
plot(getRes(a*500), title="Resistance 500", color=resColor, linewidth=2)
plot(getSup(a*500), title="Support 500", color=supColor, linewidth=2)
plot(getRes(a*750), title="Resistance 750", color=resColor, linewidth=2)
plot(getSup(a*750), title="Support 750", color=supColor, linewidth=2)
plot(getRes(a*1000), title="Resistance 1000", color=resColor, linewidth=2)
plot(getSup(a*1000), title="Support 1000", color=supColor, linewidth=2)
plot(getRes(a*1250), title="Resistance 1250", color=resColor, linewidth=2)
plot(getSup(a*1250), title="Support 1250", color=supColor, linewidth=2)
plot(getRes(a*1500), title="Resistance 1500", color=resColor, linewidth=2)
plot(getSup(a*1500), title="Support 1500", color=supColor, linewidth=2)
India VIX Based Nifty/BankNifty Range Calculator (Auto Fetch)VIX-Based Expected Daily Range (Auto Volatility Forecast)
Created by: Harshiv Symposium
📖 Purpose
This indicator automatically fetches the India VIX value and calculates the expected daily price range for major Indian indices such as Nifty and BankNifty.
It helps traders understand how much the market is likely to move today based on current volatility conditions.
Designed for educational and analytical awareness, not for signals or profit-making systems.
⚙️ Core Logic
Expected Daily Move (Range) = (India VIX × Current Index Price) ÷ Multiplier
- Multiplier for Nifty: 1000
- Multiplier for BankNifty: 700
This calculation projects the 1-standard-deviation (≈ 68% probability) and 2-standard-deviation (≈ 95% probability) movement zones for the day.
📊 Example
If India VIX = 15 and Nifty = 25,000:
Expected Move ≈ (15 × 25,000) ÷ 1000 = 375 points
Hence,
- 68% Range: 24,625 – 25,375
- 95% Range: 24,250 – 25,750
This gives traders a realistic idea of daily volatility boundaries.
🧭 Key Features
✅ Auto-Fetch India VIX
No need for manual input — automatically pulls live data from NSE:INDIAVIX.
✅ Dynamic Range Visualization
Plots upper/lower boundaries for 1σ and 2σ probability zones with shaded expected-move area.
✅ Dashboard Panel
Displays:
- Current VIX
- Expected Move (in points and %)
- Upper and Lower Ranges
✅ Smart Alerts
Alerts when price crosses upper or lower volatility range — potential breakout signal.
🎯 How It Helps
Intraday Traders:
Know the likely daily movement (e.g., ±220 pts on Nifty) and plan realistic targets or stops.
Options Traders:
Quickly assess whether it’s a seller-friendly (low VIX, small range) or buyer-friendly (high VIX, large range) session.
Risk Managers:
Use volatility context for stop-loss width and position sizing.
Breakout Traders:
If price breaks beyond the 2σ range → indicates potential volatility expansion.
💡 Interpretation Guide
Condition Market Behavior Strategy Insight
VIX ↓ ( < 14 ) Calm / Range-bound Option Selling Edge
VIX ↑ ( > 20 ) Volatile Sessions Option Buying Edge
Price within Range Stable Market Mean Reversion Setups
Price breaks Range Volatility Expansion Breakout Trades
⚠️ Disclaimer
This indicator is for educational and awareness purposes only.
It does not generate buy/sell signals or guarantee returns.
Always apply your own analysis and risk management.
ADX - Globx Options & Futures 2.0The ADX Globx Options & Futures is a custom-built trend strength indicator designed to replicate and enhance the classic Average Directional Index (ADX) model, commonly used in professional trading platforms such as IQ Option.
This version is optimized for options and futures trading, providing precise directional strength readings through adaptive smoothing and configurable parameters.
Concept and Logic
This indicator measures the strength of the current trend, regardless of its direction (bullish or bearish), by comparing directional movement between price highs and lows over a defined period.
It uses three main components:
+DI (Positive Directional Indicator): represents bullish strength.
–DI (Negative Directional Indicator): represents bearish strength.
ADX (Average Directional Index): measures the intensity of the prevailing trend, independent of direction.
The script follows the original logic proposed by J. Welles Wilder Jr., but introduces enhanced smoothing flexibility.
Users can choose between EMA (Exponential Moving Average) and Wilder’s RMA (Running Moving Average) for both DI and ADX calculations, allowing closer alignment with various platform implementations (IQ Option, MetaTrader, etc.).
How It Works
Directional Movement Calculation
The script computes upward and downward movements (+DM and –DM) by comparing the differences in highs and lows between consecutive candles.
Only positive directional changes that exceed the opposite side are considered.
This ensures each bar contributes only one valid directional movement.
True Range and Smoothing
The True Range (TR) is calculated using ta.tr(true) to include price gaps—replicating how professional derivatives platforms account for volatility jumps.
Both TR and DM values are smoothed using the selected averaging method (EMA or Wilder).
Directional Index and ADX
The smoothed +DI and –DI values are normalized over the True Range to form the Directional Index (DX), which measures the percentage difference between the two.
The ADX is then derived by smoothing the DX values, providing a stable reading of overall market strength.
Visual Representation
The ADX (white line) indicates the overall trend strength.
The +DI (dark blue) and –DI (dark red) lines show which side (bullish or bearish) is currently dominant.
Reference levels at 20 and 25 serve as strength thresholds:
Below 20 → Weak or sideways market.
Above 25 → Strong and directional trend.
Usage and Interpretation
When ADX rises above 25, the market shows a strong trend — use +DI > –DI for bullish confirmation, or the opposite for bearish momentum.
A falling ADX suggests decreasing trend strength and potential consolidation.
The default parameters (ADX Length = 34, DI Length = 34, both smoothed by EMA) match IQ Option’s internal ADX configuration, ensuring consistency between platforms.
Works on any timeframe or asset class, but is especially tuned for futures and options volatility dynamics.
Originality and Improvements
Unlike many open-source ADX indicators, this version:
Recreates IQ Option’s 34-length EMA-based ADX calculation with exact parameter alignment.
Provides selectable smoothing algorithms (EMA or Wilder) to switch between modern and classic formulations.
Uses dark-theme-optimized visuals with fine line weight and subtle contrast for clean visibility.
Maintains constant guide levels (20/25) rendered globally for precision and style compliance in Pine Script v6.
Is fully rewritten for Pine Script v6, ensuring compatibility and optimized execution.
Recommended Use
Combine with trend-following systems or breakout strategies.
Ideal for identifying market strength before engaging in options directionals or futures entries.
Use the ADX to confirm breakout momentum or filter sideways markets.
Disclaimer
This script is for educational and analytical purposes. It does not constitute financial advice or a trading signal. Users are encouraged to validate the indicator within their own trading strategies and risk frameworks.
Deviation Rate Crash SignalDescription
This indicator provides entry signals for contrarian trades that aim to capture rebounds after sharp declines, such as during market crashes.
A signal is triggered when the deviation rate from the 25-day moving average falls below -25% (default setting). On the chart, a red circle is displayed below the candlestick to indicate the signal.
Backtest (2000–2024, Nikkei 225 stocks):
Win rate: 64.73%
Payoff ratio: 1.141
Probability of ruin: 0.0% (with proper risk control)
Trading Rules (Long only):
Entry: Market buy at next day’s open when the closing price is 25% or more below the 25-day MA.
Exit: Market sell at next day’s open when:
The closing price is 10% above the entry price (take profit), or
The closing price is 10% below the entry price (stop loss), or
40 days have passed since entry.
Notes:
This indicator is tuned for crisis periods (e.g., 2008 Lehman Shock, 2011 Great East Japan Earthquake, 2020 COVID-19 crash, 2024 Yen carry trade reversal).
In normal market conditions, signals will be rare.
Pine Screener BETA Support:
Add this indicator to your favorites and scan with long condition = true.
Screener results display both the MA deviation rate and current price.
When multiple signals occur, use the deviation rate as a reference to prioritize setups.
説明
このインジケーターは、暴落時など短期間で急落した銘柄のリバウンドを狙う逆張りトレードのエントリーシグナルを提供します。
25日移動平均線からの乖離率が -25% を下回ったときにシグナルが点灯します（初期設定）。シグナルはメインチャートのローソク足の下に赤い丸印で表示されます。
バックテスト結果（2000～2024年、日経225銘柄）:
勝率: 64.73%
ペイオフレシオ: 1.141
破産確率: 0.0%（適切なリスク管理を行った場合）
トレードルール（買いのみ）:
エントリー: 終値が25日移動平均線から25%以上下方乖離した場合、翌日の寄り付きで成行買い。
手仕舞い: 翌日の寄り付きで成行売り（以下のいずれかの条件を満たした場合）
終値が買値より10%以上上昇（利確）
終値が買値より10%以上下落（損切り）
エントリーから40日経過
注意点:
このインジケーターは、2008年リーマンショック、2011年東日本大震災、2020年コロナショック、2024年円キャリートレード巻き戻しショックなど、危機的局面で効果を発揮するように調整されています。
通常の相場ではシグナルはほとんど出現しません。
Pine Screener BETA 対応:
このインジケーターをお気に入り登録し、long condition = true をフィルター条件にしてスキャンしてください。
スクリーナー結果には移動平均乖離率と現在値が表示されます。
シグナルが同時に多数出現した場合は、移動平均乖離率を参考に優先順位をつけてください。
Hilly's Advanced Crypto Scalping Strategy - 5 Min ChartTo determine the "best" input parameters for the Advanced Crypto Scalping Strategy on a 5-minute chart, we need to consider the goals of optimizing for profitability, minimizing false signals, and adapting to the volatile nature of cryptocurrencies. The default parameters in the script are a starting point, but the optimal values depend on the specific cryptocurrency pair, market conditions, and your risk tolerance. Below, I'll provide recommended input values based on common practices in crypto scalping, along with reasoning for each parameter. I’ll also suggest how to fine-tune them using TradingView’s backtesting and optimization tools.
Recommended Input Parameters
These values are tailored for a 5-minute chart for liquid cryptocurrencies like BTC/USD or ETH/USD on exchanges like Binance or Coinbase. They aim to balance signal frequency and accuracy for day trading.
Fast EMA Length (emaFastLen): 9
Reasoning: A 9-period EMA is commonly used in scalping to capture short-term price movements while remaining sensitive to recent price action. It reacts faster than the default 10, aligning with the 5-minute timeframe.
Slow EMA Length (emaSlowLen): 21
Reasoning: A 21-period EMA provides a good balance for identifying the broader trend on a 5-minute chart. It’s slightly longer than the default 20 to reduce noise while confirming the trend direction.
RSI Length (rsiLen): 14
Reasoning: The default 14-period RSI is a standard choice for momentum analysis. It works well for detecting overbought/oversold conditions without being too sensitive on short timeframes.
RSI Overbought (rsiOverbought): 75
Reasoning: Raising the overbought threshold to 75 (from 70) reduces false sell signals in strong bullish trends, which are common in crypto markets.
RSI Oversold (rsiOversold): 25
Reasoning: Lowering the oversold threshold to 25 (from 30) filters out weaker buy signals, ensuring entries occur during stronger reversals.
MACD Fast Length (macdFast): 12
Reasoning: The default 12-period fast EMA for MACD is effective for capturing short-term momentum shifts in crypto, aligning with scalping goals.
MACD Slow Length (macdSlow): 26
Reasoning: The default 26-period slow EMA is a standard setting that works well for confirming momentum trends without lagging too much.
MACD Signal Smoothing (macdSignal): 9
Reasoning: The default 9-period signal line is widely used and provides a good balance for smoothing MACD crossovers on a 5-minute chart.
Bollinger Bands Length (bbLen): 20
Reasoning: The default 20-period Bollinger Bands are effective for identifying volatility breakouts, which are key for scalping in crypto markets.
Bollinger Bands Multiplier (bbMult): 2.0
Reasoning: A 2.0 multiplier is standard and captures most price action within the bands. Increasing it to 2.5 could reduce signals but improve accuracy in highly volatile markets.
Stop Loss % (slPerc): 0.8%
Reasoning: A tighter stop loss of 0.8% (from 1.0%) suits the high volatility of crypto, helping to limit losses on false breakouts while keeping risk manageable.
Take Profit % (tpPerc): 1.5%
Reasoning: A 1.5% take-profit target (from 2.0%) aligns with scalping’s goal of capturing small, frequent gains. Crypto markets often see quick reversals, so a smaller target increases the likelihood of hitting profits.
Use Candlestick Patterns (useCandlePatterns): True
Reasoning: Enabling candlestick patterns (e.g., engulfing, hammer) adds confirmation to signals, reducing false entries in choppy markets.
Use Volume Filter (useVolumeFilter): True
Reasoning: The volume filter ensures signals occur during high-volume breakouts, which are more likely to sustain in crypto markets.
Signal Arrow Size (signalSize): 2.0
Reasoning: Increasing the arrow size to 2.0 (from 1.5) makes buy/sell signals more visible on the chart, especially on smaller screens or volatile price action.
Background Highlight Transparency (bgTransparency): 85
Reasoning: A slightly higher transparency (85 from 80) keeps the background highlights subtle but visible, avoiding chart clutter.
How to Apply These Parameters
Copy the Script: Use the Pine Script provided in the previous response.
Paste in TradingView: Open TradingView, go to the Pine Editor, paste the code, and click "Add to Chart."
Set Parameters: In the strategy settings, manually input the recommended values above or adjust them via the input fields.
Test on a 5-Minute Chart: Apply the strategy to a liquid crypto pair (e.g., BTC/USDT, ETH/USDT) on a 5-minute chart.
Fine-Tuning for Optimal Performance
To find the absolute best parameters for your specific trading pair and market conditions, use TradingView’s Strategy Tester and optimization features:
Backtesting:
Run the strategy on historical data for your chosen pair (e.g., BTC/USDT on Binance).
Check metrics like Net Profit, Profit Factor, Win Rate, and Max Drawdown in the Strategy Tester.
Focus on a sample period of at least 1–3 months to capture various market conditions (bull, bear, sideways).
Parameter Optimization:
In the Strategy Tester, click the settings gear next to the strategy name.
Enable optimization for key inputs like emaFastLen (test range: 7–12), emaSlowLen (15–25), slPerc (0.5–1.5), and tpPerc (1.0–3.0).
Run the optimization to find the combination with the highest net profit or best Sharpe ratio, but avoid over-optimization (curve-fitting) by testing on out-of-sample data.
Market-Specific Adjustments:
Volatile Pairs (e.g., DOGE/USDT): Use tighter stop losses (e.g., 0.5–0.7%) and smaller take-profit targets (e.g., 1.0–1.2%) to account for rapid price swings.
Stable Pairs (e.g., BTC/USDT): Slightly wider stop losses (e.g., 1.0–1.2%) and take-profit targets (e.g., 1.8–2.5%) may work better.
Trending Markets: Increase emaSlowLen (e.g., to 25–30) to follow stronger trends.
Choppy Markets: Tighten bbMult (e.g., to 1.8) and lower rsiOverbought/rsiOversold (e.g., to 70/30) to filter noise.
Forward Testing:
After backtesting, run the strategy in real-time on a demo account for 1–2 weeks to validate performance under current market conditions.
Important Notes
No Universal "Best" Numbers: Optimal parameters vary by crypto pair, exchange, and market conditions (e.g., bull vs. bear market). The suggested values are a starting point based on common scalping practices.
Risk Management: Never risk more than 1–2% of your account per trade. Adjust position sizing in TradingView’s strategy settings to align with your risk tolerance.
Market Volatility: Crypto markets are highly volatile. Test the strategy on your specific pair and adjust parameters if signals are too frequent or too rare.
Not Financial Advice: This is an educational tool. Backtest thoroughly and use a demo account before trading with real funds.
If you want me to generate a chart showing backtest performance for specific parameters or to tweak the script further (e.g., adding more indicators or adjusting signal logic), let me know!can you add clear buy ans sell signs above the candles in this codeTo add clear buy and sell signals above the candles in the provided Pine Script, I'll modify the existing code to include prominent text labels directly above (for sell signals) and below (for buy signals) the candles. These labels will be larger, color-coded, and positioned to avoid clutter, ensuring they are easily visible on the chart. The core strategy logic (EMA, RSI, MACD, Bollinger Bands, candlestick patterns, and volume filter) will remain unchanged, and I'll reuse the same artifact_id since this is an update to the previous script.AdvancedCryptoScalpingStrategy.pinex-pine
Market Spiralyst [Hapharmonic]Hello, traders and creators! 👋
Market Spiralyst: Let's change the way we look at analysis, shall we? I've got to admit, I scratched my head on this for weeks, Haha :). What you're seeing is an exploration of what's possible when code meets art on financial charts. I wanted to try blending art with trading, to do something new and break away from the same old boring perspectives. The goal was to create a visual experience that's not just analytical, but also relaxing and aesthetically pleasing.
This work is intended as a guide and a design example for all developers, born from the spirit of learning and a deep love for understanding the Pine Script™ language. I hope it inspires you as much as it challenged me!
🧐 Core Concept: How It Works
Spiralyst is built on two distinct but interconnected engines:
The Generative Art Engine: At its core, this indicator uses a wide range of mathematical formulas—from simple polygons to exotic curves like Torus Knots and Spirographs—to draw beautiful, intricate shapes directly onto your chart. This provides a unique and dynamic visual backdrop for your analysis.
The Market Pulse Engine: This is where analysis meets art. The engine takes real-time data from standard technical indicators (RSI and MACD in this version) and translates their states into a simple, powerful "Pulse Score." This score directly influences the appearance of the "Scatter Points" orbiting the main shape, turning the entire artwork into a living, breathing representation of market momentum.
🎨 Unleash Your Creativity! This Is Your Playground
We've included 25 preset shapes for you... but that's just the starting point !
The real magic happens when you start tweaking the settings yourself. A tiny adjustment can make a familiar shape come alive and transform in ways you never expected.
I'm genuinely excited to see what your imagination can conjure up! If you create a shape you're particularly proud of or one that looks completely unique, I would love to see it. Please feel free to share a screenshot in the comments below. I can't wait to see what you discover! :)
Here's the default shape to get you started:
The Dynamic Scatter Points: Reading the Pulse
This is where the magic happens! The small points scattered around the main shape are not just decorative; they are the visual representation of the Market Pulse Score.
The points have two forms:
A small asterisk (`*`): Represents a low or neutral market pulse.
A larger, more prominent circle (`o`): Represents a high, strong market pulse.
Here’s how to read them:
The indicator calculates the Pulse Strength as a percentage (from 0% to 100%) based on the total score from the active indicators (RSI and MACD). This percentage determines the ratio of circles to asterisks.
High Pulse Strength (e.g., 80-100%): Most of the scatter points will transform into large circles (`o`). This indicates that the underlying momentum is strong and It could be an uptrend. It's a visual cue that the market is gaining strength and might be worth paying closer attention to.
Low Pulse Strength (e.g., 0-20%): Most or all of the scatter points will remain as small asterisks (`*`). This suggests weak, neutral, or bearish momentum.
The key takeaway: The more circles you see, the stronger the bullish momentum is according to the active indicators. Watch the artwork "breathe" as the circles appear and disappear with the market's rhythm!
And don't worry about the shape you choose; the scatter points will intelligently adapt and always follow the outer boundary of whatever beautiful form you've selected.
How to Use
Getting started with Spiralyst is simple:
Choose Your Canvas: Start by going into the settings and picking a `Shape` and `Palette` from the "Shape Selection & Palette" group that you find visually appealing. This is your canvas.
Tune Your Engine: Go to the "Market Pulse Engine" settings. Here, you can enable or disable the RSI and MACD scoring engines. Want to see the pulse based only on RSI? Just uncheck the MACD box. You can also fine-tune the parameters for each indicator to match your trading style.
Read the Vibe: Observe the scatter points. Are they mostly small asterisks or are they transforming into large, vibrant circles? Use this visual feedback as a high-level gauge of market momentum.
Check the Dashboard: For a precise breakdown, look at the "Market Pulse Analysis" table on the top-right. It gives you the exact values, scores, and total strength percentage.
Explore & Experiment: Play with the different shapes and color palettes! The core analysis remains the same, but the visual experience can be completely different.
⚙️ Settings & Customization
Spiralyst is designed to be highly customizable.
Shape Selection & Palette: This is your main control panel. Choose from over 25 unique shapes, select a color palette, and adjust the line extension style ( `extend` ) or horizontal position ( `offsetXInput` ).
scatterLabelsInput: This setting controls the total number of points (both asterisks and circles) that orbit the main shape. Think of it as adjusting the density or visual granularity of the market pulse feedback.
The Market Pulse engine will always calculate its strength as a percentage (e.g., 75%). This percentage is then applied to the `scatterLabelsInput` number you've set to determine how many points transform into large circles.
Example: If the Pulse Strength is 75% and you set this to `100` , approximately 75 points will become circles. If you increase it to `200` , approximately 150 points will transform.
A higher number provides a more detailed, high-resolution view of the market pulse, while a lower number offers a cleaner, more minimalist look. Feel free to adjust this to your personal visual preference; the underlying analytical percentage remains the same.
Market Pulse Engine:
`⚙️ RSI Settings` & `⚙️ MACD Settings`: Each indicator has its own group.
Enable Scoring: Use the checkbox at the top of each group to include or exclude that indicator from the Pulse Score calculation. If you only want to use RSI, simply uncheck "Enable MACD Scoring."
Parameters: All standard parameters (Length, Source, Fast/Slow/Signal) are fully adjustable.
Individual Shape Parameters (01-25): Each of the 25+ shapes has its own dedicated group of settings, allowing you to fine-tune every aspect of its geometry, from the number of petals on a flower to the windings of a knot. Feel free to experiment!
For Developers & Pine Script™ Enthusiasts
If you are a developer and wish to add more indicators (e.g., Stochastic, CCI, ADX), you can easily do so by following the modular structure of the code. You would primarily need to:
Add a new `PulseIndicator` object for your new indicator in the `f_getMarketPulse()` function.
Add the logic for its scoring inside the `calculateScore()` method.
The `calculateTotals()` method and the dashboard table are designed to be dynamic and will automatically adapt to include your new indicator!
One of the core design philosophies behind Spiralyst is modularity and scalability . The Market Pulse engine was intentionally built using User-Defined Types (UDTs) and an array-based structure so that adding new indicators is incredibly simple and doesn't require rewriting the main logic.
If you want to add a new indicator to the scoring engine—let's use the Stochastic Oscillator as a detailed example—you only need to modify three small sections of the code. The rest of the script, including the adaptive dashboard, will update automatically.
Here’s your step-by-step guide:
#### Step 1: Add the User Inputs
First, you need to give users control over your new indicator. Find the `USER INTERFACE: INPUTS` section and add a new group for the Stochastic settings, right after the MACD group.
Create a new group name: `string GRP_STOCH = "⚙️ Stochastic Settings"`
Add the inputs: Create a boolean to enable/disable it, and then add the necessary parameters (`%K`, `%D`, `Smooth`). Use the `active` parameter to link them to the enable/disable checkbox.
// Add this code block right after the GRP_MACD and MACD inputs
string GRP_STOCH = "⚙️ Stochastic Settings"
bool stochEnabledInput = input.bool(true, "Enable Stochastic Scoring", group = GRP_STOCH)
int stochKInput = input.int(14, "%K Length", minval=1, group = GRP_STOCH, active = stochEnabledInput)
int stochDInput = input.int(3, "%D Smoothing", minval=1, group = GRP_STOCH, active = stochEnabledInput)
int stochSmoothInput = input.int(3, "Smooth", minval=1, group = GRP_STOCH, active = stochEnabledInput)
#### Step 2: Integrate into the Pulse Engine (The "Factory")
Next, go to the `f_getMarketPulse()` function. This function acts as a "factory" that builds and configures the entire market pulse object. You need to teach it how to build your new Stochastic indicator.
Update the function signature: Add the new `stochEnabledInput` boolean as a parameter.
Calculate the indicator: Add the `ta.stoch()` calculation.
Create a `PulseIndicator` object: Create a new object for the Stochastic, populating it with its name, parameters, calculated value, and whether it's enabled.
Add it to the array: Simply add your new `stochPulse` object to the `array.from()` list.
Here is the complete, updated `f_getMarketPulse()` function :
// Factory function to create and calculate the entire MarketPulse object.
f_getMarketPulse(bool rsiEnabled, bool macdEnabled, bool stochEnabled) =>
// 1. Calculate indicator values
float rsiVal = ta.rsi(rsiSourceInput, rsiLengthInput)
= ta.macd(close, macdFastInput, macdSlowInput, macdSignalInput)
float stochVal = ta.sma(ta.stoch(close, high, low, stochKInput), stochDInput) // We'll use the main line for scoring
// 2. Create individual PulseIndicator objects
PulseIndicator rsiPulse = PulseIndicator.new("RSI", str.tostring(rsiLengthInput), rsiVal, na, 0, rsiEnabled)
PulseIndicator macdPulse = PulseIndicator.new("MACD", str.format("{0},{1},{2}", macdFastInput, macdSlowInput, macdSignalInput), macdVal, signalVal, 0, macdEnabled)
PulseIndicator stochPulse = PulseIndicator.new("Stoch", str.format("{0},{1},{2}", stochKInput, stochDInput, stochSmoothInput), stochVal, na, 0, stochEnabled)
// 3. Calculate score for each
rsiPulse.calculateScore()
macdPulse.calculateScore()
stochPulse.calculateScore()
// 4. Add the new indicator to the array
array indicatorArray = array.from(rsiPulse, macdPulse, stochPulse)
MarketPulse pulse = MarketPulse.new(indicatorArray, 0, 0.0)
// 5. Calculate final totals
pulse.calculateTotals()
pulse
// Finally, update the function call in the main orchestration section:
MarketPulse marketPulse = f_getMarketPulse(rsiEnabledInput, macdEnabledInput, stochEnabledInput)
#### Step 3: Define the Scoring Logic
Now, you need to define how the Stochastic contributes to the score. Go to the `calculateScore()` method and add a new case to the `switch` statement for your indicator.
Here's a sample scoring logic for the Stochastic, which gives a strong bullish score in oversold conditions and a strong bearish score in overbought conditions.
Here is the complete, updated `calculateScore()` method :
// Method to calculate the score for this specific indicator.
method calculateScore(PulseIndicator this) =>
if not this.isEnabled
this.score := 0
else
this.score := switch this.name
"RSI" => this.value > 65 ? 2 : this.value > 50 ? 1 : this.value < 35 ? -2 : this.value < 50 ? -1 : 0
"MACD" => this.value > this.signalValue and this.value > 0 ? 2 : this.value > this.signalValue ? 1 : this.value < this.signalValue and this.value < 0 ? -2 : this.value < this.signalValue ? -1 : 0
"Stoch" => this.value > 80 ? -2 : this.value > 50 ? 1 : this.value < 20 ? 2 : this.value < 50 ? -1 : 0
=> 0
this
#### That's It!
You're done. You do not need to modify the dashboard table or the total score calculation.
Because the `MarketPulse` object holds its indicators in an array , the rest of the script is designed to be adaptive:
The `calculateTotals()` method automatically loops through every indicator in the array to sum the scores and calculate the final percentage.
The dashboard code loops through the `enabledIndicators` array to draw the table. Since your new Stochastic indicator is now part of that array, it will appear automatically when enabled!
---
Remember, this is your playground! I'm genuinely excited to see the unique shapes you discover. If you create something you're proud of, feel free to share it in the comments below.
Happy analyzing, and may your charts be both insightful and beautiful! 💛
Asistente de Barra de Estado ADX
// This is an all-in-one indicator designed to visually represent the market environment
// based on the G2 (trend-following) and SMOG (reversal/ranging) trading systems.
// It replaces the need for a separate ADX indicator.
//
// FEATURES:
//
// 1. Multi-Timeframe ADX:
// - 5-Minute ADX (Blue Line - The "Referee"): Determines the overall market environment (Trending or Ranging).
// - 1-Minute ADX (Yellow Line - The "Trigger"): Measures immediate momentum for trade entries.
//
// 2. Environment Background Coloring:
// The indicator's own background panel changes color to provide an instant signal:
// - Green: G2 Bullish Environment (5-min ADX > 25 & Price is Trending Up)
// - Red: G2 Bearish Environment (5-min ADX > 25 & Price is Trending Down)
// - Gray: Gray Zone (Indecisive/Risky Market, 5-min ADX between 20-25)
// - Blue: SMOG Environment (Weak/Ranging Market, 5-min ADX < 20)
//
// 3. Reference Lines:
// Includes horizontal lines at the key 20 and 25 levels for easy reference.
//
// HOW TO USE:
// Use this indicator as the primary tool to decide whether to look for a G2
// (trend-following) or a SMOG (reversal) setup.
//
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
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Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
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Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
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OpenAI Signal Generator - Enhanced Accuracy# AI-Powered Trading Signal Generator Guide
## Overview
This is an advanced trading signal generator that combines multiple technical indicators using AI-enhanced logic to generate high-accuracy trading signals. The indicator uses a sophisticated combination of RSI, MACD, Bollinger Bands, EMAs, ADX, and volume analysis to provide reliable buy/sell signals with comprehensive market analysis.
## Key Features
### 1. Multi-Indicator Analysis
- **RSI (Relative Strength Index)**
- Length: 14 periods (default)
- Overbought: 70 (default)
- Oversold: 30 (default)
- Used for identifying overbought/oversold conditions
- **MACD (Moving Average Convergence Divergence)**
- Fast Length: 12 (default)
- Slow Length: 26 (default)
- Signal Length: 9 (default)
- Identifies trend direction and momentum
- **Bollinger Bands**
- Length: 20 periods (default)
- Multiplier: 2.0 (default)
- Measures volatility and potential reversal points
- **EMAs (Exponential Moving Averages)**
- Fast EMA: 9 periods (default)
- Slow EMA: 21 periods (default)
- Used for trend confirmation
- **ADX (Average Directional Index)**
- Length: 14 periods (default)
- Threshold: 25 (default)
- Measures trend strength
- **Volume Analysis**
- MA Length: 20 periods (default)
- Threshold: 1.5x average (default)
- Confirms signal strength
### 2. Advanced Features
- **Customizable Signal Frequency**
- Daily
- Weekly
- 4-Hour
- Hourly
- On Every Close
- **Enhanced Filtering**
- EMA crossover confirmation
- ADX trend strength filter
- Volume confirmation
- ATR-based volatility filter
- **Comprehensive Alert System**
- JSON-formatted alerts
- Detailed technical analysis
- Multiple timeframe analysis
- Customizable alert frequency
## How to Use
### 1. Initial Setup
1. Open TradingView and create a new chart
2. Select your preferred trading pair
3. Choose an appropriate timeframe
4. Apply the indicator to your chart
### 2. Configuration
#### Basic Settings
- **Signal Frequency**: Choose how often signals are generated
- Daily: Signals at the start of each day
- Weekly: Signals at the start of each week
- 4-Hour: Signals every 4 hours
- Hourly: Signals every hour
- On Every Close: Signals on every candle close
- **Enable Signals**: Toggle signal generation on/off
- **Include Volume**: Toggle volume analysis on/off
#### Technical Parameters
##### RSI Settings
- Adjust `rsi_length` (default: 14)
- Modify `rsi_overbought` (default: 70)
- Modify `rsi_oversold` (default: 30)
##### EMA Settings
- Fast EMA Length (default: 9)
- Slow EMA Length (default: 21)
##### MACD Settings
- Fast Length (default: 12)
- Slow Length (default: 26)
- Signal Length (default: 9)
##### Bollinger Bands
- Length (default: 20)
- Multiplier (default: 2.0)
##### Enhanced Filters
- ADX Length (default: 14)
- ADX Threshold (default: 25)
- Volume MA Length (default: 20)
- Volume Threshold (default: 1.5)
- ATR Length (default: 14)
- ATR Multiplier (default: 1.5)
### 3. Signal Interpretation
#### Buy Signal Requirements
1. RSI crosses above oversold level (30)
2. Price below lower Bollinger Band
3. MACD histogram increasing
4. Fast EMA above Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
#### Sell Signal Requirements
1. RSI crosses below overbought level (70)
2. Price above upper Bollinger Band
3. MACD histogram decreasing
4. Fast EMA below Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
### 4. Visual Indicators
#### Chart Elements
- **Moving Averages**
- SMA (Blue line)
- Fast EMA (Yellow line)
- Slow EMA (Purple line)
- **Bollinger Bands**
- Upper Band (Green line)
- Middle Band (Orange line)
- Lower Band (Green line)
- **Signal Markers**
- Buy Signals: Green triangles below bars
- Sell Signals: Red triangles above bars
- **Background Colors**
- Light green: Buy signal period
- Light red: Sell signal period
### 5. Alert System
#### Alert Types
1. **Signal Alerts**
- Generated when buy/sell conditions are met
- Includes comprehensive technical analysis
- JSON-formatted for easy integration
2. **Frequency-Based Alerts**
- Daily/Weekly/4-Hour/Hourly/Every Close
- Includes current market conditions
- Technical indicator values
#### Alert Message Format
```json
{
"symbol": "TICKER",
"side": "BUY/SELL/NONE",
"rsi": "value",
"macd": "value",
"signal": "value",
"adx": "value",
"bb_upper": "value",
"bb_middle": "value",
"bb_lower": "value",
"ema_fast": "value",
"ema_slow": "value",
"volume": "value",
"vol_ma": "value",
"atr": "value",
"leverage": 10,
"stop_loss_percent": 2,
"take_profit_percent": 5
}
```
## Best Practices
### 1. Signal Confirmation
- Wait for multiple confirmations
- Consider market conditions
- Check volume confirmation
- Verify trend strength with ADX
### 2. Risk Management
- Use appropriate position sizing
- Implement stop losses (default 2%)
- Set take profit levels (default 5%)
- Monitor market volatility
### 3. Optimization
- Adjust parameters based on:
- Trading pair volatility
- Market conditions
- Timeframe
- Trading style
### 4. Common Mistakes to Avoid
1. Trading without volume confirmation
2. Ignoring ADX trend strength
3. Trading against the trend
4. Not considering market volatility
5. Overtrading on weak signals
## Performance Monitoring
Regularly review:
1. Signal accuracy
2. Win rate
3. Average profit per trade
4. False signal frequency
5. Performance in different market conditions
## Disclaimer
This indicator is for educational purposes only. Past performance is not indicative of future results. Always use proper risk management and trade responsibly. Trading involves significant risk of loss and is not suitable for all investors.
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
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Schaff Trend Cycle (STC)The STC (Schaff Trend Cycle) indicator is a momentum oscillator that combines elements of MACD and stochastic indicators to identify market cycles and potential trend reversals.
Key features of the STC indicator:
Oscillates between 0 and 100, similar to a stochastic oscillator
Values above 75 generally indicate overbought conditions
Values below 25 generally indicate oversold conditions
Signal line crossovers (above 75 or below 25) can suggest potential entry/exit points
Faster and more responsive than traditional MACD
Designed to filter out market noise and identify cyclical trends
Traders typically use the STC indicator to:
Identify potential trend reversals
Confirm existing trends
Generate buy/sell signals when combined with other technical indicators
Filter out false signals in choppy market conditions
This STC implementation includes multiple smoothing options that act as filters:
None: Raw STC values without additional smoothing, which provides the most responsive but potentially noisier signals.
EMA Smoothing: Applies a 3-period Exponential Moving Average to reduce noise while maintaining reasonable responsiveness (default).
Sigmoid Smoothing: Transforms the STC values using a sigmoid (S-curve) function, creating more gradual transitions between signals and potentially reducing whipsaw trades.
Digital (Schmitt Trigger) Smoothing: Creates a binary output (0 or 100) with built-in hysteresis to prevent rapid switching.
The STC indicator uses dynamic color coding to visually represent momentum:
Green: When the STC value is above its 5-period EMA, indicating positive momentum
Red: When the STC value is below its 5-period EMA, indicating negative momentum
The neutral zone (25-75) is highlighted with a light gray fill to clearly distinguish between normal and extreme readings.
Alerts:
Bullish Signal Alert:
The STC has been falling
It bottoms below the 25 level
It begins to rise again
This pattern helps confirm potential uptrend starts with higher reliability.
Bearish Signal Alert:
The STC has been rising
It peaks above the 75 level
It begins to decline
This pattern helps identify potential downtrend starts.
ADX + DMI (HMA Version)📝 Description (What This Indicator Does)
🚀 ADX + DMI (HMA Version) is a trend strength oscillator that enhances the traditional ADX by using the Hull Moving Average (HMA) instead of EMA.
✅ This results in a much faster and more responsive trend detection while filtering out choppy price action.
🎯 What This Indicator Does:
1️⃣ Measures Trend Strength – ADX shows when a trend is strong or weak.
2️⃣ Identifies Trend Direction – DI+ (Green) shows bullish momentum, DI- (Red) shows bearish momentum.
3️⃣ Uses Hull Moving Average (HMA) for Faster Signals – Removes lag and reacts faster to trend changes.
4️⃣ Reduces False Signals – Traditional ADX lags behind, but this version reacts quickly to reversals.
5️⃣ Good for Scalping & Day Trading – Especially for BTC 5-min and lower timeframes.
⚙ Indicator Inputs (Customization)
Input Name Example Value Purpose
ADX Length 14 Defines the smoothing for the ADX value.
DI Length 14 Defines how DI+ and DI- are calculated.
HMA Length 24 Hull Moving Average smoothing for ADX & DI+.
Trend Threshold 25 The level above which ADX confirms a strong trend.
📌 You can adjust these settings to optimize for different assets and timeframes.
🎯 Trading Rules & How to Use It
✅ How to Identify a Strong Trend:
When ADX (Blue Line) is above 25→ A strong trend is in play.
When ADX is below 25 → The market is choppy or ranging.
✅ How to Use DI+ and DI- for Trend Direction:
If DI+ (Green) is above DI- (Red), the market is in an uptrend.
If DI- (Red) is above DI+ (Green), the market is in a downtrend.
✅ How to Confirm Entries & Exits:
1️⃣ Enter Long when DI+ crosses above DI- while ADX is rising above 25.
2️⃣ Enter Short when DI- crosses above DI+ while ADX is rising above 25.
3️⃣ Avoid trading when ADX is below 25 – the market is in a choppy range.
This should not be used as a stand alone oscillator. Trading takes skill and is risky. Use at your own risk.
This is not advise on how to trade, these are just examples of how I use the oscillator. Trade at your own risk.
You can put this on your chart versus the tradingview adx and you can adjust the settings to see the difference. This was optimized for btc on the 5 min chart. You can adjust for your trading strategy.
BTCUSD with adjustable sl,tpThis strategy is designed for swing traders who want to enter long positions on pullbacks after a short-term trend shift, while also allowing immediate short entries when conditions favor downside movement. It combines SMA crossovers, a fixed-percentage retracement entry, and adjustable risk management parameters for optimal trade execution.
Key Features:
✅ Trend Confirmation with SMA Crossover
The 10-period SMA crossing above the 25-period SMA signals a bullish trend shift.
The 10-period SMA crossing below the 25-period SMA signals a bearish trend shift.
Short trades are only taken if the price is below the 150 EMA, ensuring alignment with the broader trend.
📉 Long Pullback Entry Using Fixed Percentage Retracement
Instead of entering immediately on the SMA crossover, the strategy waits for a retracement before going long.
The pullback entry is defined as a percentage retracement from the recent high, allowing for an optimized entry price.
The retracement percentage is fully adjustable in the settings (default: 1%).
A dynamic support level is plotted on the chart to visualize the pullback entry zone.
📊 Short Entry Rules
If the SMA(10) crosses below the SMA(25) and price is below the 150 EMA, a short trade is immediately entered.
Risk Management & Exit Strategy:
🚀 Take Profit (TP) – Fully customizable profit target in points. (Default: 1000 points)
🛑 Stop Loss (SL) – Adjustable stop loss level in points. (Default: 250 points)
🔄 Break-Even (BE) – When price moves in favor by a set number of points, the stop loss is moved to break-even.
📌 Extra Exit Condition for Longs:
If the SMA(10) crosses below SMA(25) while the price is still below the EMA150, the strategy force-exits the long position to avoid reversals.
How to Use This Strategy:
Enable the strategy on your TradingView chart (recommended for stocks, forex, or indices).
Customize the settings – Adjust TP, SL, BE, and pullback percentage for your risk tolerance.
Observe the plotted retracement levels – When the price touches and bounces off the level, a long trade is triggered.
Let the strategy manage the trade – Break-even protection and take-profit logic will automatically execute.
Ideal Market Conditions:
✅ Trending Markets – The strategy works best when price follows strong trends.
✅ Stocks, Indices, or Forex – Can be applied across multiple asset classes.
✅ Medium-Term Holding Period – Suitable for swing trades lasting days to weeks.
